Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex

Depression vulnerability and gray matter integrity of interoceptive networks in remitted depressed outpatients

BACKGROUND

Interoception, the representation of internal body states, plays an important role in mental health. While functional neuroimaging links Major Depressive Disorder (MDD) relapse vulnerability to stress-induced inhibition of sensorimotor regions, its association with structural changes in interoceptive networks remains unclear.

METHODS

A secondary analysis explored relationships between gray matter volume and relapse vulnerability in remitted MDD patients (N = 85), with two data acquisitions surrounding eight-weeks of prophylactic psychotherapy followed by a two-year follow-up. Participants were randomly assigned to either Cognitive Behavioral Therapy or Mindfulness-Based Cognitive Therapy (MBCT). Mixed-effects models were applied to study the relationships between cortical thickness, time, and intervention type with clinical variables such as relapse status, residual symptoms, and decentering, adjusting for relevant covariates. Analyses were conducted at whole brain levels as well as in pre-defined regions of interest, focusing on sensory regions implicated by prior research.

RESULTS

Relapse was consistently linked to greater cortical thickness in the left superior circular sulcus of the insula and the left anterior occipital sulcus. Residual symptoms correlated with increased cortical thickness in the left insula and right precentral regions, while decentering was linked to reduced thickness in the middle temporal and inferior parietal regions. MBCT participants showed greater cortical thickness increases in the right superior temporal gyrus over time.

CONCLUSIONS

MDD vulnerability was unexpectedly linked to greater cortical thickness in sensory and prefrontal brain regions, suggesting that depression vulnerability may reflect maladaptive skill acquisition. MBCT may promote gray matter growth in the right superior temporal region.

TRIAL REGISTRATION

ClinicalTrials.govNCT01178424.

Common Variants in PLXNA4 and Correlation to Neuroimaging Phenotypes in Healthy, Mild Cognitive Impairment, and Alzheimer's Disease Cohorts

A comprehensive genome-wide association study (GWAS) has validated the identification of the Plexin-A 4 (PLXNA4) gene as a novel susceptibility factor for Alzheimer's disease (AD). Nonetheless, the precise role of PLXNA4 gene polymorphisms in the pathophysiology of AD remains to be established. Consequently, this study is aimed at exploring the relationship between PLXNA4 gene polymorphisms and neuroimaging phenotypes intimately linked to AD. This study encompassed 812 subjects with PLXNA4 genotype data, procured from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Employing a tagging strategy, we identified five common variant sites within the PLXNA4 gene and assessed their associations with glucose metabolism, atrophy in AD-related brain regions (including the medial temporal lobe, hippocampus, and parahippocampal gyrus), and intracerebral Aβ deposition. We conducted a comprehensive analysis using a multiple linear regression model, with neuroimaging phenotypes as the dependent variable and PLXNA4 gene polymorphisms as the independent variable while incorporating APOE e4 carrier status, education level, age, and gender as covariates. The subjects were stratified into three groups based on their disease status: the Alzheimer's disease (AD) group, the mild cognitive impairment (MCI) group, and the cognitively normal healthy control (CN) group. Within each group, we examined the associations between PLXNA4 gene polymorphisms and various neuroimaging phenotypes. Our study identified significant associations between the rs156676-A and rs78036292-G alleles and the baseline volumes of the anterior cingulate and middle temporal gyrus, respectively, across the entire population. After 1 year of follow-up, a significant correlation was observed between the rs6467431-G allele and accelerated volumetric atrophy of the parahippocampal gyrus in the overall population. Additionally, at the 2-year follow-up, significant correlations were observed between three PLXNA4 loci (rs1863015, rs6467431, rs67468325) and volumetric atrophy in the anterior cingulate, middle temporal gyrus, and hippocampus across the entire population. Specifically, the rs1863015-G allele notably accelerated atrophy of the left middle temporal gyrus and bilateral hippocampus, whereas the A alleles of rs6467431 and rs67468325 markedly accelerated atrophy specifically in the bilateral hippocampus. Subgroup analysis further validated these findings. Additionally, in the baseline CN group, the rs78036292 allele showed a significant correlation with intracerebral Aβ deposition, while in the 2-year follow-up CN group, rs67468325 was significantly associated with alterations in glucose metabolism rates in the right cingulate gyrus. Our findings indicate that PLXNA4 genotypes may modulate the development of AD through their regulation of intracerebral Aβ deposition. Additionally, PLXNA4 genotypes are strongly associated with AD-related brain atrophy and glucose metabolism, suggesting that they may alter susceptibility to AD by modulating neurodegenerative biomarkers.

"Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for tasks like cortical registration, parcellation, and thickness estimation. Traditionally, such analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a T1-weighted scan with 1 mm resolution. This requirement precludes application of these techniques to most MRI scans acquired for clinical purposes, since they are often anisotropic and lack the required T1-weighted contrast. To overcome this limitation and enable large-scale neuroimaging studies using vast amounts of existing clinical data, we introduce recon-all-clinical, a novel methodology for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs), and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We evaluated recon-all-clinical on multiple public datasets like ADNI, HCP, AIBL, OASIS and including a large clinical dataset of over 9,500 scans. The results indicate that our method produces geometrically precise cortical reconstructions across different MRI contrasts and resolutions, consistently achieving high accuracy in parcellation. Cortical thickness estimates are precise enough to capture aging effects, independently of MRI contrast, even though accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.

Late-onset GM2 gangliosidosis: magnetic resonance imaging, diffusion tensor imaging, and correlational fiber tractography differentiate Tay-Sachs and Sandhoff diseases

GM2 gangliosidosis is lysosomal storage disorder caused by deficiency of the heterodimeric enzyme β-hexosaminidase A. Tay-Sachs disease is caused by variants in HEXA encoding the α-subunit and Sandhoff disease is caused by variants in HEXB encoding the β-subunit. Due to shared clinical and biochemical findings, the two have been considered indistinguishable. We applied T1-weighted volumetric analysis, diffusion tensor imaging (DTI), and correlational fiber tractography to assess phenotypic differences in these two diseases. 51 T1-weighted and 40 DTI scans from 19 Late-Onset GM2 patients with either late-onset Sandhoff disease (LOSD), or late-onset Tay-Sachs (LOTS) were included and compared to 1033 neurotypical control volumetric MRI scans. LOTS patients had significantly smaller cerebellum volume compared to neurotypical controls (p < 0.0001) and LOSD patients (p < 0.0001). There was no statistical difference for the volume of any structure between LOSD and neurotypical controls. DTI analysis showed LOTS patients had higher mean diffusivity (MD) in the left cerebellum (p = 0.003703), right cerebellum (p = 0.003435), superior cerebellar peduncle (p = 0.007332), and vermis (p = 0.01007) compared to LOSD. LOTS patients had lower fractional anisotropy (FA) in the left cerebellum (p = 0.005537), right cerebellum (p = 0.01905), SCP (p = 0.02844), and vermis (p = 0.02469) when compared to LOSD. Correlational fiber tractography identified fiber tracts in cerebellar pathways with higher FA and lower MD in LOSD patients compared to LOTS patients. Our study shows neurobiologic differences between these two related disorders. To our knowledge, this is the first study using correlational tractography in a lysosomal storage disorder. This result indicates a greater burden of cerebellar pathology in LOTS patients compared with LOSD patients.

Flexible nonlinear modeling reveals age-related differences in resting-state functional brain connectivity in autistic males from childhood to mid-adulthood

BACKGROUND

Divergent age-related functional brain connectivity in autism spectrum disorder (ASD) has been observed using resting-state fMRI, although the specific findings are inconsistent across studies. Common statistical regression approaches that fit identical models across functional brain networks may contribute to these inconsistencies. Relationships among functional networks have been reported to follow unique nonlinear developmental trajectories, suggesting the need for flexible modeling. Here we apply generalized additive models (GAMs) to flexibly adapt to distinct network trajectories and simultaneously describe divergent age-related changes from childhood into mid-adulthood in ASD.

METHODS

1107 males, aged 5-40, from the ABIDE I & II cross-sectional datasets were analyzed. Functional connectivity was extracted using a network-based template. Connectivity values were harmonized using COMBAT-GAM. Connectivity-age relationships were assessed with thin-plate spline GAMs. Post-hoc analyses defined the age-ranges of divergent aging in ASD.

RESULTS

Typically developing (TD) and ASD groups shared 15 brain connections that significantly changed with age (FDR-corrected p < 0.05). Network connectivity exhibited diverse nonlinear age-related trajectories across the functional connectome. Comparing ASD and TD groups, default mode to central executive between-network connectivity followed similar nonlinear paths with no group differences. Contrarily, the ASD group had chronic hypoconnectivity throughout default mode-ventral attentional (salience) and default mode-somatomotor aging trajectories. Within-network somatomotor connectivity was similar between groups in childhood but diverged in adolescence with the ASD group showing decreased within-network connectivity. Network connectivity between the somatomotor network and various other functional networks had fully disrupted age-related pathways in ASD compared to TD, displaying significantly different model curvatures and fits.

LIMITATIONS

The present analysis includes only male participants and has a restricted age range, limiting analysis of early development and later life aging, years 40 and beyond. Additionally, our analysis is limited to large-scale network cortical functional parcellation. To parse more specificity of brain region connectivity, a fine-grained functional parcellation including subcortical areas may be warranted.

CONCLUSION

Flexible non-linear modeling minimizes statistical assumptions and allows diagnosis-related brain connections to follow independent data-driven age-related pathways. Using GAMs, we describe complex age-related pathways throughout the human connectome and observe distinct periods of divergence in autism.

Depression is marked by differences in structural covariance between deep-brain nuclei and sensorimotor cortex

BACKGROUND

Depression impacts nearly 3% of the global adult population. Symptomatology is likely related to regions encompassing frontoparietal, somatosensory, and salience networks. Questions regarding deep brain nuclei (DBN), including the substantia nigra (STN), subthalamic nucleus (STN), and red nucleus (RN) remain unanswered.

METHODS

Using an existing structural neuroimaging dataset including 86 individuals (Baranger et al., 2021; nDEP = 39), frequentist and Bayesian logistic regressions assessed whether DBN volumes predict diagnosis, then structural covariance analyses in FreeSurfer tested diagnostic differences in deep brain volume and cortical morphometry covariance. Exploratory correlations tested relationships between implicated cortical regions and Hamilton Depression Rating Scale (HAM-D) scores.

RESULTS

Group differences emerged in deep brain/cortical covariance. Right RN volume covaried with left parietal operculum volume and central sulcus thickness, while left RN and right STN volumes covaried with right occipital pole volume. Positive relationships were observed within the unaffected group and negative relationships among those with depression. These cortical areas did not correlate with HAM-D scores. Simple DBN volumes did not predict diagnostic group.

CONCLUSION

Structural codependence between DBN and cortical regions may be important in depression, potentially for sensorimotor features. Future work should focus on causal mechanisms of DBN involvement with sensory integration.

Limbic system abnormalities in episodic cluster headache: a 7T MRI multimodal study

BACKGROUND

Although the limbic system has long been thought to be involved in the pathophysiology of cluster headache, inconsistencies in imaging studies of episodic cluster headache (eCH) patients and limited understanding of the specific regions within the limbic system have prevented a full explanation of its involvement in the disease. Therefore, we performed multimodal imaging analysis using 7 T MRI with the aim of exploring structural-functional abnormalities in subregions of the limbic system and their relationship with clinical features.

METHODS

In this cross-sectional study, we employed 7T MRI to investigate structural (volumetric) and functional (fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo)) alterations in limbic subregions (hypothalamus, thalamus, amygdala, hippocampus) among 69 in-bout but outside the attacks eCH patients and 63 healthy controls (HCs). Automated volumetry and resting-state functional MRI analyses were performed after adjusting for age, Generalized Anxiety Disorder scale, sex (and intracranial volume when evaluating volumetric measures). Then functional-structural coupling indices were computed to assess network-level relationships.

RESULTS

In eCH patients, volumes in right anterior inferior and right posterior of hypothalamus, left molecular_layer_hippocampal-head, left lateral-nucleus and left Central-nucleus on the headache side, as well as left tuberal inferior and left tuberal superior of hypothalamus, and right parasubiculum on the contralateral side were significantly altered compared with HCs (P < 0.05). Additionally, the volume of the right anterior inferior was positively correlated with the duration of last headache episode. After false discovery rate correction, widespread alterations in fALFF and ReHo values were observed among hypothalamic, thalamic, hippocampal, and amygdalar subregions, some of which correlated with clinical measures. Furthermore, the structure-function coupling indices in the right anterior inferior and the left lateral geniculate nucleus on the headache side differed significantly between eCH patients and HCs.

CONCLUSIONS

Our findings demonstrate that in-bout but outside the attacks eCH patients present anatomical and functional maladaptation of the limbic system. Moreover, the observed dissociation between localized abnormalities and largely preserved network coupling-except in the hypothalamus and thalamus-suggests that these two regions may be particularly susceptible to eCH-related dysfunction, while broader brain networks retain compensatory capacity in pathological states. These findings refine potential neuromodulation targets and highlight the value of ultrahigh-field imaging in eCH research.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease

Identifying individuals with early-stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would enable better-informed medical, support and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. Across heterogeneous clinical phenotypes, patients with atypical AD consistently exhibit abnormal tau accumulation in the posterior nodes of the default mode network of the cerebral cortex. This evidence suggests that tau burden in this functional network could be a common imaging biomarker for prognostication across the syndromic spectrum of AD. Here, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes: Posterior Cortical Atrophy (n = 16); logopenic variant Primary Progressive Aphasia (n = 15); and amnestic syndrome with multi-domain impairment and young age of onset < 65 years (n = 17). All patients underwent MRI, tau PET and amyloid PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Atypical early AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, P < 0.001, Cohen's d = 0.95. Across clinical phenotypes, baseline tau in the default mode network was the strongest predictor of clinical decline (R2 = 0.30), outperforming a simpler model with baseline clinical impairment and demographic variables (R2 = 0.10), tau in other functional networks (R2 = 0.11-0.26) and the magnitude of cortical atrophy (R2 = 0.20) and amyloid burden (R2 = 0.09) in the default mode network. Overall, these findings point to the contribution of default mode network tau to predicting the magnitude of clinical decline in atypical early AD patients 1 year later. This simple measure could aid the development of a personalized prognostic, monitoring and treatment plan, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis

BACKGROUND

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.

METHODS

We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum.

RESULTS

We found volBrain's vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer's recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain's vol2Brain and SimNIBS' headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL's FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.

CONCLUSIONS

Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Scale-dependent brain age with higher-order statistics from structural magnetic resonance imaging

Inferring chronological age from magnetic resonance imaging (MRI) brain data has become a valuable tool for the early detection of neurodegenerative diseases. We present a method inspired by cosmological techniques for analyzing galaxy surveys, utilizing higher-order summary statistics with multivariate two- and three-point analyses in 3D Fourier space. This method identifies outliers while offering physiological interpretability, allowing the detection of scales where brain anatomy differs across age groups and providing insights into brain aging processes. Similarly to the evolution of cosmic structures, the brain structure also evolves naturally but displays contrasting behaviors at different scales. On larger scales, structure loss occurs with age, possibly due to ventricular expansion, while smaller scales show increased structure, likely related to decreased cortical thickness and gray/white matter volume. Using MRI data from the OASIS-3 database for the complete sample of 864 sessions (reduced sample: 827 sessions), our method predicts chronological age with a Mean Absolute Error (MAE) of 3.8 years (~3.6 years) for individuals aged ~40-100 (50-85), while providing information as a function of scale. A neural density posterior estimation shows that the 1- σ uncertainty for each individual varies between ~3 and 7 years, suggesting that, beyond sample variance, complex genetic or lifestyle-related factors may influence brain aging. Applying this method to an independent database, Cam-CAN, validates our analysis, yielding a MAE of ~3.4 for the age range from 18 to 88 years. This work demonstrates the utility of interdisciplinary research, bridging cosmological methods and neuroscience.

Morphometric Measurement of Mean Cortical Curvature: Analysis of Alterations in Cognitive Impairment

Background and Objectives: Cognitive impairment, including mild cognitive impairment (MCI) and Alzheimer's disease (AD), is a growing public health concern. Early detection and an understanding of structural changes are crucial for accurate diagnosis and timely intervention. Cortical curvature, a morphometric measure derived from structural magnetic resonance imaging (MRI), has emerged as a potential biomarker for neurodegenerative processes. This study investigates the relationship between mean cortical curvature and cognitive impairment. Materials and Methods: A cross-sectional study was conducted with 58 participants, categorized into, first, cognitively impaired (CI) and non-cognitively impaired (NC) groups and, second, a normal cognitive group (NC), a mild cognitive performance group (MPG), and a low cognitive performance group (LPG) based on the Montreal Cognitive Assessment (MoCA) score. MRI data were acquired using a 3.0 Tesla scanner, and cortical reconstruction was performed using FreeSurfer 7.2.0. Mean cortical curvature values were extracted for 34 brain regions per hemisphere. Results: Significant differences in mean cortical curvature were found between the CI and NC groups. In the right hemisphere, statistically significant changes in mean curvature were observed in the isthmus cingulate (U = 188.5, p = 0.006), lingual (U = 202.5, p = 0.013), pars orbitalis (U = 221.5, p = 0.031), and posterior cingulate regions (U = 224.5, p = 0.035). In the left hemisphere, significant differences were detected in the cuneus (U = 226.5, p = 0.038) and posterior cingulate (U = 231.5, p = 0.046) regions. Analysis across three cognitive performance groups (NC, MPG, and LPG) showed significant curvature differences in the right isthmus cingulate (H(2) = 7.492, p = 0.024) and lingual regions (H(2) = 6.250, p = 0.044). Conclusions: Decreased mean cortical curvature in brain regions associated with cognitive function could be indicative of cognitive impairment and may reflect early neurodegenerative changes. These results highlight cortical curvature as a potential structural sign for cognitive impairment, showing the need for further investigation in longitudinal studies.

Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept

Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer's disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for reliably detecting longitudinal atrophy, particularly over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual patients. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2'23"). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over two days. Using linear mixed-effects models, phenotypically vulnerable cortical ("core atrophy") regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

Retrospective assessment of clinical global impression of severity and change in GM1 gangliosidosis: a tool to score natural history data in rare disease cohorts

BACKGROUND

Clinical trials for rare diseases pose unique challenges warranting alternative approaches in demonstrating treatment efficacy. Such trials face challenges including small patient populations, variable onset of symptoms and rate of disease progression, and ethical considerations, particularly in neurodegenerative diseases. In this study, we present the retrospective clinical global impression (RCGI) severity and change (RCGI-S/C) scale on 27 patients with GM1 gangliosidosis, a post hoc clinician-rated outcome measure to evaluate natural history study participants as historical controls for comparisons with treated patients in a clinical trial.

METHODS

We conducted a systematic chart review of 27 GM1 gangliosidosis natural history participants across 95 total visits. RCGI-S was assessed at the first visit and rated 1 (normal) to 7 (among the most extremely ill). Each subsequent follow-up was rated on the RCGI-C scale from 1 (very much improved) to 7 (very much worse). We demonstrate scoring guidelines of both scales with examples and justifications for this pilot in GM1 gangliosidosis natural history participants. The convergent validity of the RCGI scales was explored through correlations with magnetic resonance imaging (MRI) and the Vineland Adaptive Behavioral Scales.

RESULTS

We found strong association between the RCGI-S scores with gray matter volume (r(14) = -0.81; 95% CI [-0.93, -0.51], p < 0.001), and RCGI-C scores significantly correlated with increases in ventricular volume (χ2(1) = 18.6, p < 0.001). Baseline RCGI-S scores also strongly correlated with Vineland adaptive behavioral composite scores taken at the same visit (r(14) = -0.72; 95% CI [-0.93, -0.17], p = 0.02).

CONCLUSION

RCGI-S/C scales, which use the clinical evaluation to assess the severity of disease of each patient visit over time, were consolidated into a single quantitative metric in this study. Longitudinal RCGI-C scores allowed us to quantify disease progression in our late-infantile and juvenile GM1 patients. We suggest that the retrospective CGI may be an important tool in evaluating historical data for comparison with changes in disease progression/mitigation following therapeutic interventions.

Understanding the link between insulin resistance and cognition: a cross-sectional study conducted in an urban, South Indian cohort

BACKGROUND

Recent research suggests that metabolic dysregulation caused by insulin resistance (IR) can have a negative impact on cognition. Therefore, the objective of this study is to explore the role of IR as an independent metabolic risk for decreased cognitive performance.

METHODS

The study included 1072 non-demented participants aged 45 years and above were recruited from Tata Longitudinal Study of Aging (TLSA). Fasting insulin and blood glucose levels were collected during the baseline visit. HOMA-IR formula was used to calculate IR. Cognition was assessed using the COGNITO neuropsychological test battery. Generalized Linear Regression Model (GLM) was performed to find the relationship between IR category and COGNITO battery. The brain imaging was conducted using a 3 Tesla MRI system. The cortical volumes were acquired using Freesurfer software (v7.2.0) (Salgado et al. Arq Gastroenterol 47(2):165-169, 2010). Further, GLM analysis was performed for MRI variables.

RESULTS

The estimated general prevalence of IR in our study is 56.3%. Model 1 suggested that IR is associated with reduced auditory attention (p = 0.014), and word comprehension (p = 0.043) tasks. Model 2 and 4 showed that there is an association with IR and poorerauditory attention (p = 0.015; p = 0.012) task. However, there was no significant association found in model 3. GLM analysis for MRI indicated that IR is associated with reduced brain volumes in left hemisphere like amygdala (p = 0.0012), inferior temporal lobe (p = 0.002), lateral orbitofrontal cortex (p = 0.005), superior temporal insula (p = 0.017), middle temporal lobe (p = 0.002), entorhinal (p = 0.049), and right hemisphere brain volumes like precuneus (p = 0.025), and insula (p = 0.002).

CONCLUSIONS

Our study findings conclude IR is significantly associated with poorer cognitive performance related to auditory attention. Furthermore, the study also revealed that IR is associated with decreased brain volumes in specific regions.

Effects of kynurenine pathway metabolites on choroid plexus volume, hemodynamic response, and spontaneous neural activity: A new mechanism for disrupted neurovascular communication and impaired cognition in mood disorders

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) involve alterations of immune-inflammatory setpoints that activate the kynurenine pathway (KP), affecting serotoninergic and glutamatergic neurotransmission through indoleamine-2,3-dioxygenase (IDO) activity. This process produces metabolites like Kynurenine (Kyn), 3-Hydroxykynurenine (3-HK), Quinolinic acid (QuinA), and Kynurenic acid (KynA), these last two acting as agonist and antagonist at glutamatergic N-methyl-D-aspartate receptors (NMDARs), respectively. NMDARs, expressed in the choroid plexus (ChP) and arteriolar smooth muscle cells, regulate blood-brain-barrier permeability and cerebral artery dilation, suggesting that KP may influence neurovascular coupling, aligning blood flow with neural energy demand. KP's role in modulating vascular tone supports this hypothesis. Altered fractional amplitude of low-frequency fluctuations (fALFF) and disrupted default mode network (DMN) activity in mood disorders are linked to cognitive deficits possibly through neurovascular uncoupling like in neurological diseases. This makes fALFF and hemodynamic response function (HRF) potential indicators of these changes. We investigated KP associations with ChP volumes, functional-MRI at rest measures like spontaneous neural activity (fALFF) and hemodynamic response function (HRF) parameters within the default mode network (DMN), and cognitive performance in 42 MDD and 36 BD inpatients experiencing a depressive episode. Results revealed that lower QuinA/KynA ratios and higher KynA levels predict larger ChP volumes. Higher KYN and 3-HK levels, along with lower KynA levels, were associated with increased DMN fALFF and shorter time-to-peak (TTP) in HRF, suggesting altered neurovascular coupling. Mediation analyses indicated that KP metabolites influenced cognitive performance through their effects on resting state measures, affecting global cognitive functioning score, verbal fluency, and psychomotor coordination. These findings suggest that KP metabolites modulate brain function and structure via NMDAR-mediated pathways and vascular-based mechanisms, offering insights into the cognitive impairments observed in mood disorders and identifying potential therapeutic targets.

Patterns of cortical thickness in MCI patients with and without semantic impairment

BACKGROUND

About half of MCI patients experience semantic deficits, which may predict progression to Alzheimer's disease (AD). The neural basis of these deficits in MCI is not well understood. This study aimed to examine the relationship between semantic memory performance and cortical thickness in MCI patients.

METHODS

Using FreeSurfer, T1-weighted MRI scans were analyzed from MCI patients with (MCIsem+) and without (MCIsem-) semantic deficits. Correlation analyses across all participants, including healthy controls, examined the link between semantic memory and cortical thickness, controlling for age and education. Group comparisons of cortical thickness were also conducted between MCIsem+ and MCIsem- groups.

RESULTS

Significant correlations emerged between semantic memory performance and cortical thickness in the left medial temporal lobe, right temporal pole, and bilateral frontal regions-areas involved in central semantic and executive processes. Additionally, MCIsem + patients showed reduced cortical thickness in frontal, parietal, and occipital areas compared to MCIsem- patients.

CONCLUSION

Semantic memory performance in MCI patients is associated with structural differences in regions supporting both central and executive aspects of semantic processing. Given that MCIsem + patients may face higher risk of AD progression, longitudinal studies should investigate these cortical markers' predictive value.

Associations between structural brain changes and blood neurofilament light chain protein in treatment-resistant schizophrenia

OBJECTIVE

Around 30% of people with schizophrenia are refractory to antipsychotic treatment (treatment-resistant schizophrenia). Abnormal structural neuroimaging findings, in particular volume and thickness reductions, are often described in schizophrenia. Novel biomarkers of active brain pathology such as neurofilament light chain protein are now expected to improve current understanding of psychiatric disorders, including schizophrenia. This study explored whether treatment-resistant schizophrenia individuals exhibit different associations between plasma neurofilament light chain protein levels and regional cortical thickness reductions compared with controls.

METHODS

Plasma neurofilament light chain protein levels were measured, and T1-weighted magnetic resonance imaging sequences were obtained and processed via FreeSurfer for each participant. General linear models adjusting for age and body mass index were estimated to determine whether the interaction between diagnostic group and plasma neurofilament light chain protein level predicted lower cortical thickness across frontotemporal structures and the insula.

RESULTS

A total of 79 participants were included: 37 treatment-resistant schizophrenia and 42 healthy controls. Significant (false discovery rate-corrected) cortical thinning of the left (p = 0.005, η2p = 0.100) and right (p = 0.002, η2p = 0.149) insula, and left inferior temporal gyrus (p < 0.001, η2p = 0.143) was associated with higher levels of plasma neurofilament light chain protein in treatment-resistant schizophrenia, but not in healthy controls.

CONCLUSIONS

The association between regional thickness reduction of the bilateral insula and left inferior temporal gyrus with plasma neurofilament light chain protein may reflect a neuroprogressive component to schizophrenia, which is not observed in the normal population.

Topologically Optimized Intrinsic Brain Networks

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

ChatGPT-estimated occupational complexity predicts cognitive outcomes and cortical thickness above and beyond socioeconomic status among older adults

Many aging cohort studies have collected data on participants' job titles, yet these job titles were seldom analyzed within the cognitive aging context despite their relevance to neurocognition, due to difficulties in analyzing these job titles quantitatively. While it is possible to rate these jobs' occupational complexity (OC) using job classification systems, this can be somewhat labor-intensive and prone to human errors. To this end, we demonstrate a novel and simple method to extract OC ratings from job titles using ChatGPT. Then, we showcased the utility of these ratings in predicting cognitive and structural brain outcomes, especially compared to other socioeconomic status (SES) indicators. Community-dwelling older adults (N = 238, agemean = 70) completed cognitive assessments and underwent MRI scans. Regression models were fitted to predict 14 different cognitive outcomes, vertex-wise cortical thickness (CT), and subcortical gray matter volumes, using OC scores and/or SES predictors (e.g., education, housing type, and income levels), controlling for demographical covariates. OC scores outperformed SES indicators in predicting clusters of CT increases and most cognitive outcomes, including diagnoses of mild cognitive impairment. Furthermore, OC scores significantly predicted clusters of CT increases and various cognitive outcomes, even after controlling for SES. Meta-analytic decoding suggests these clusters of CT increases occurred in regions typically associated with sensorimotor and memory processing. These results highlight the significant and unique contribution of ChatGPT-derived OC scores in predicting cognitive and brain aging outcomes. These scores are easy to derive and can be helpful in fine-tuning predictions of cognitive and brain aging outcomes.

A Case for Automated Segmentation of MRI Data in Milder Neurodegenerative Diseases

Background

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared 5 fully automated segmentation pipelines including FSL, Freesurfer, volBrain, SPM12, and SimNIBS with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.

Methods

We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared results for 7 brain structures including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum.

Results

We found volBrain's vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer's recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain's vol2Brain and SimNIBS' headreco to have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL's FIRST to give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.

Conclusion

Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process including the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Cognitive and brain health in juvenile myoclonic epilepsy: Role of social determinants of health

OBJECTIVE

Juvenile myoclonic epilepsy (JME) is a prevalent genetic generalized epilepsy with linked abnormalities in cognition, behavior, and brain structure. Well recognized is the potential for advancing understanding of the epigenetic contributions to the neurobehavioral complications of JME, but to date there has been no examination of the role of socioeconomic disadvantage in regard to the cognitive and brain health of JME, which is the focus of this investigation.

METHODS

Seventy-seven patients with JME and 44 unrelated controls underwent neuropsychological assessment, structural neuroimaging, and clinical interview to delineate epilepsy history and aspects of family status. The Area Deprivation Index characterized the presence and degree of neighborhood disadvantage, which was examined in relation to cognitive factor scores underlying a comprehensive neuropsychological test battery, academic metrics, integrity of brain structure, and family characteristics.

RESULTS

JME participants resided in neighborhoods associated with significantly more socioeconomic disadvantage, which was associated with significantly poorer performance across all three cognitive factor scores and reading fluency. JME was associated with significant reduction of total subcortical gray matter (GM) but not total cortical gray or white matter volumes. Among controls, participants residing in more advantaged areas exhibited increased volumes of total subcortical GM and diverse subcortical structures as well as areas of increased cortical thickness and volume in frontal/prefrontal regions, findings that were compromised or not evident in JME, raising the possibility of disease-related attenuation of socioeconomic advantage.

SIGNIFICANCE

Socioeconomic disadvantage in JME is associated with adverse effects on cognitive and academic status, whereas socioeconomic advantage in controls is associated with increased brain volumes and thickness, markers of brain health that were largely attenuated or absent in JME. The associations detected here argue for the need to better integrate the social determinants of health with genetic and epigenetic factors in advancing understanding of cognitive and brain health in JME.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

BACKGROUND

Hippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies.

METHODS

A retrospective study from Hainan Children's Hospital database (2020-2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection.

RESULTS

The study included 114 ASD patients (98 boys, 2-8 years; 16 girls, 2-6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis.

CONCLUSION

Random forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

Alteration in Cortical Structure Mediating the Impact of Blood Oxygen-Carrying Capacity on Gross Motor Skills in Infants With Complex Congenital Heart Disease

Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12-26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen-carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen-carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen-carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen-carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.

Heterogeneous clinical phenotypes of sporadic early-onset Alzheimer's disease: a neuropsychological data-driven approach

BACKGROUND

The clinical presentations of early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort.

METHODS

Neuropsychological test performance from 169 amyloid-positive EOAD participants were analyzed. Education-corrected normative comparisons were made using a sample of 98 cognitively normal participants. Comparing the relative levels of impairment between each cognitive domain, we applied a cut-off of 1 SD below all other domain scores to indicate a phenotype of "predominant" impairment in a given cognitive domain. Individuals were otherwise considered to have multidomain impairment. Whole-cortex general linear modeling of cortical atrophy was applied as an MRI-based validation of these distinct clinical phenotypes.

RESULTS

We identified 6 phenotypic subtypes of EOAD: Dysexecutive Predominant (22% of sample), Amnestic Predominant (11%), Language Predominant (11%), Visuospatial Predominant (15%), Mixed Amnestic/Dysexecutive Predominant (11%), and Multidomain (30%). These phenotypes did not differ by age, sex, or years of education. The APOE ɛ4 genotype was enriched in the Amnestic Predominant group, who were also rated as least impaired. Cortical thickness analysis validated these clinical phenotypes with dissociations in atrophy patterns observed between the Dysexecutive and Amnestic Predominant groups. In contrast to the heterogeneity observed from our neuropsychological data-driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic-predominant, 9% were non-amnestic, 4% met criteria for Posterior Cortical Atrophy, and 5% met criteria for Primary Progressive Aphasia.

CONCLUSION

A neuropsychological data-driven method to phenotype EOAD individuals uncovered a more detailed understanding of the presenting heterogeneity in this atypical AD sample compared to clinical judgment alone. Clinicians and patients may over-report memory dysfunction at the expense of non-memory symptoms. These findings have important implications for diagnostic accuracy and treatment considerations.

Stable Cortical Body Maps Before and After Arm Amputation

The adult brain's capacity for cortical reorganization remains debated. Using longitudinal neuroimaging in three adults, followed up to five years before and after arm amputation, we compared cortical activity elicited by movement of the hand (pre-amputation) versus phantom hand (post-amputation) and lips (pre/post-amputation). We observed stable representations of both hand and lips. By directly quantifying activity changes across amputation, we overturn decades of animal and human research, demonstrating amputation does not trigger large-scale cortical reorganization.

Characterizing the microstructural transition at the gray matter-white matter interface: Implementation and demonstration of age-associated differences

BACKGROUND

The cortical gray matter-white matter interface (GWI) is a natural transition zone where the composition of brain tissue abruptly changes and is a location for pathologic change in brain disorders. While diffusion magnetic resonance imaging (dMRI) is a reliable and well-established technique to characterize brain microstructure, the GWI is difficult to assess with dMRI due to partial volume effects and is normally excluded from such studies.

METHODS

In this study, we introduce an approach to characterize the dMRI microstructural profile across the GWI and to assess the sharpness of the microstructural transition from cortical gray matter (GM) to white matter (WM). This analysis includes cross-sectional data from a total of 146 participants (18-91 years; mean age: 52.4 (SD 21.4); 83 (57 %) female) enrolled in two normative lifespan cohorts at Albert Einstein College of Medicine from 2019 to 2023. We compute the aggregate GWI slope for each parameter, across each of 6 brain regions (cingulate, frontal, occipital, orbitofrontal, parietal, and temporal) for each participant. The association of GWI slope in each region with age was assessed using a linear model, with biological sex as a covariate.

RESULTS

We demonstrate this method captures an inherent change in fractional anisotropy (FA), axial diffusivity (AD), orientation dispersion index (ODI) and intracellular volume fraction (ICVF) across the GWI that is characterized by small variance. We identified statistically significant associations of FA slope with age in all regions (p < 0.002 for all analyses), with FA slope magnitude inversely associated with higher age. Similar statistically significant age-related associations were found for AD slope in cingulate, occipital, and temporal regions, for ODI slope in parietal and occipital regions, and for ICVF slope in frontal, orbitofrontal, parietal, and temporal regions.

CONCLUSION

The inverse association of slope magnitude with age indicates loss of the sharp GWI transition in aging, which is consistent with processes such as dendritic pruning, axonal degeneration, and inflammation. This method overcomes techniques issues related to interrogating the GWI. Beyond characterizing normal aging, it could be applied to explore pathological effects at this crucial, yet under-researched region.

Exploring predementia: Understanding the characteristics of subjective cognitive decline plus from India

BACKGROUND

Subjective cognitive decline (SCD) is the early predementia syndrome. that occurs even before the development of objective cognitive decline. SCD plus refers to an additional set of criteria that increases the likelihood of developing mild cognitive impairment and further progressing to Alzheimer's disease (AD). Studying the progression of SCD-plus participants will help in understanding the importance of diagnosing this condition at an early stage and delaying its onset.

OBJECTIVE

The present tries to examine neurocognitive changes in individuals who met the criteria of SCD-plus patients. The study also investigated the imaging correlates of these individuals in both cohorts.

METHODS

This study included 94 participants from Srinivaspura Aging, Neuro Senescence, and COGnition (SANSCOG) and Tata Longitudinal Study of Aging (TLSA) cohorts who satisfy the criteria of SCD plus. Mann-Whitney U test was used to compare the SCD plus participants and healthy controls. Regression analysis was performed to find the association between SCD plus and cognition.

RESULTS

The SCD-plus group performed poorer than the healthy group in episodic memory delayed recall (p = 0.049), name face recognition (p = 0.023), and letter fluency (p = 0.004) tasks. The generalized linear model revealed that the SCD-plus group had lower left cerebellar cortex (p = 0.010) and right inferior occipital cortex (p = 0.016) volumes than the healthy control group.

CONCLUSIONS

The participants in the SCD-plus group performed poorly on memory and language-related tasks, and the volumes of the associated brain regions decreased. This study suggested that the SCD-plus group had characteristics similar to AD group and can help in identifying AD at the earliest.

Abnormal choroid plexus, hippocampus, and lateral ventricles volumes as markers of treatment-resistant major depressive disorder

AIM

One-third of patients with major depressive disorder (MDD) do not achieve full remission and have high relapse rates even after treatment, leading to increased medical costs and reduced quality of life and health status. The possible specificity of treatment-resistant depression (TRD) neurobiology is still under investigation, with risk factors such as higher inflammatory markers being identified. Given recent findings on the role of choroid plexus (ChP) in neuroinflammation and hippocampus in treatment response, the aim of the present study was to evaluate inflammatory- and trophic-related differences in these regions along with ventricular volumes among patients with treatment-sensitive depression (TSD), TRD, and healthy controls (HCs).

METHODS

ChP, hippocampal, and ventricular volumes were assessed in 197 patients with MDD and 58 age- and sex-matched HCs. Volumes were estimated using FreeSurfer 7.2. Treatment resistance status was defined as failure to respond to at least two separate antidepressant treatments. Region of interest volumes were then compared among groups.

RESULTS

We found higher ChP volumes in patients with TRD compared with patients with TSD and HCs. Our results also showed lower hippocampal volumes and higher lateral ventricular volumes in TRD compared with both patients without TRD and HCs.

CONCLUSIONS

These findings corroborate the link between TRD and neuroinflammation, as ChP volume could be considered a putative marker of central immune activity. The lack of significant differences in all of the region of interest volumes between patients with TSD and HCs may highlight the specificity of these features to TRD, possibly providing new insights into the specific neurobiological underpinnings of this condition.

Dissociable spatial topography of cortical atrophy in early-onset and late-onset Alzheimer's disease: A head-to-head comparison of the LEADS and ADNI cohorts

INTRODUCTION

Early-onset and late-onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head-to-head comparison of cortical atrophy in EOAD and LOAD, using two large and well-characterized cohorts (LEADS and ADNI).

METHODS

We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.e., early-stage AD), along with cognitively unimpaired participants.

RESULTS

After controlling for the level of cognitive impairment, we found a double dissociation between AD clinical phenotype and localization/magnitude of atrophy, characterized by predominant neocortical involvement in EOAD and more focal anterior medial temporal involvement in LOAD.

DISCUSSION

Our findings point to the clinical utility of MRI-based biomarkers of atrophy in differentiating between EOAD and LOAD, which may be useful for diagnosis, prognostication, and treatment.

HIGHLIGHTS

Early-onset Alzheimer's disease (EOAD) and late-onset AD (LOAD) patients showed distinct and overlapping cortical atrophy patterns. EOAD patients showed prominent atrophy in widespread neocortical regions. LOAD patients showed prominent atrophy in the anterior medial temporal lobe. Regional atrophy was correlated with the severity of global cognitive impairment. Results were comparable when the sample was stratified for mild cognitive impairment (MCI) and dementia.

Individual differences in intolerance of uncertainty is primarily linked to the structure of inferior frontal regions

Increased intolerance of uncertainty (IU), or distress felt when encountering situations with unknown outcomes, occurs transdiagnostically across various forms of psychopathology and is targeted in therapeutic intervention. Increased intolerance of uncertainty shows overlap with symptoms of internalizing disorders, such as depression and anxiety, including negative affect and anxious apprehension (worry). While neuroanatomical correlates of IU have been reported, previous investigations have not disentangled the specific neural substrates of IU above and beyond any overlapping relationships with aspects of internalizing psychopathology. The current study did so in a sample of 42 adults and 79 adolescents, who completed questionnaires assessing IU and internalizing symptoms, and underwent structural MRI. When controlling for internalizing symptoms, across adults and adolescents, specific associations of IU were found with the structure of the inferior frontal cortex and orbitofrontal cortex, regions implicated in cognitive control and emotional valuation/regulation. In addition, in adolescents, associations were observed with rostral middle frontal cortex and portions of the cingulate cortex. No associations were observed with threat-related regions, such as the amygdala. Potential cognitive/emotional mechanisms that might explain the association between individual differences in intolerance of uncertainty and morphology of the inferior frontal cortex are discussed.

Amygdala Nuclei Atrophy in Cognitive Impairment and Dementia: Insights from High-Resolution Magnetic Resonance Imaging

Background and Objectives: Cognitive impairment affects memory, reasoning, and problem-solving, with early detection being critical for effective management. The amygdala, a key structure in emotional processing and memory, may play a pivotal role in detecting cognitive decline. This study examines differences in amygdala nuclei volumes in patients with varying levels of cognitive performance to evaluate its potential as a biomarker. Material and methods: This cross-sectional study of 35 participants was conducted and classified into three groups: the normal (≥26), moderate (15-25), and low (≤14) cognitive performance groups based on the Montreal Cognitive Assessment (MoCA) scores. High-resolution magnetic resonance imaging at 3.0 T scanner was used to assess amygdala nuclei volumes. Results: Significant amygdala atrophy was observed in multiple amygdala nuclei across cognitive performance groups, with more pronounced changes in the low-performance group. The right hemisphere nuclei, including the lateral and basal nuclei, showed more significant differences, indicating their sensitivity to cognitive decline. Conclusions: This study highlights the potential of amygdala nuclei atrophy as a biomarker for cognitive impairment. Additional research with larger sample sizes and longitudinal designs is needed to confirm these findings and determine their diagnostic value.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Objectives

Brain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet ( B aby and I nfant B rain S egmentation Neural Net work), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images.

Experimental Design

Included in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes.

Principal Observations

BIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics.

Conclusions

BIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies

Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large sample sizes for developing accurate predictive models. However, multicenter studies, incorporating varying research participant characteristics, MRI scanners, and imaging acquisition protocols, may introduce confounding factors, potentially hindering the creation of generalizable machine learning models. Models developed using one dataset may not readily apply to another, emphasizing the importance of classification model generalizability in multi-scanner and multicenter studies for producing reproducible results. This study focuses on enhancing generalizability in classifying individual migraine patients and healthy controls using brain MRI data through a data harmonization strategy. We propose identifying a 'healthy core'-a group of homogeneous healthy controls with similar characteristics-from multicenter studies. The Maximum Mean Discrepancy (MMD) in Geodesic Flow Kernel (GFK) space is employed to compare two datasets, capturing data variabilities and facilitating the identification of this 'healthy core'. Homogeneous healthy controls play a vital role in mitigating unwanted heterogeneity, enabling the development of highly accurate classification models with improved performance on new datasets. Extensive experimental results underscore the benefits of leveraging a 'healthy core'. We utilized two datasets: one comprising 120 individuals (66 with migraine and 54 healthy controls), and another comprising 76 individuals (34 with migraine and 42 healthy controls). Notably, a homogeneous dataset derived from a cohort of healthy controls yielded a significant 25% accuracy improvement for both episodic and chronic migraineurs.

Wearable non-invasive neuroprosthesis for targeted sensory restoration in neuropathy

Peripheral neuropathy (PN), the most common complication of diabetes, leads to sensory loss and associated health issues as pain and increased fall risk. However, present treatments do not counteract sensory loss, but only partially manage its consequences. Electrical neural stimulation holds promise to restore sensations, but its efficacy and benefits in PN damaged nerves are yet unknown. We designed a wearable sensory neuroprosthesis (NeuroStep) providing targeted neurostimulation of the undamaged nerve portion and assessed its functionality in 14 PN participants. Our system partially restored lost sensations in all participants through a purposely calibrated neurostimulation, despite PN nerves being less sensitive than healthy nerves (N = 22). Participants improved cadence and functional gait and reported a decrease of neuropathic pain after one day. Restored sensations activated cortical patterns resembling naturally located foot sensations. NeuroStep restores real-time intuitive sensations in PN participants, holding potential to enhance functional and health outcomes while advancing effective non-invasive neuromodulation.

Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging

Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Age-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses

Previous studies on age-related changes in cortical and hippocampal morphology were not designed or able to reveal the complex spatial patterns of changes across the lifespan. To this end, the current study examined these changes in a decade-by-decade manner by comparing consecutive age decades at the vertex-wise level. Additionally, the lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled and plotted out to provide an overview of their age-related changes. Using two lifespan datasets (Ntotal = 1378; 18 ≤ age ≤ 100), vertex-wise thickness and surface area measurements were extracted from the cortical and unfolded hippocampal surfaces and analyzed using whole-brain/hippocampus vertex-wise analyses. Lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled with generalized additive models for location, scale, and shape. These models revealed fairly linear declines in both cortical measures and inverted U-shaped trajectories for both hippocampal measures. Across the different age decades, the sizes and locations of cortical thinning clusters were highly variable across the age decades. No significant clusters of cortical surface area changes were observed across the age decades. Significant clusters of hippocampal surface area and thickness reduction were not observed until the 70s. Generally, the agreement between datasets on the hippocampal findings was much higher than those of the cortical surface. These findings revealed important nuances in the age-related changes of cortical and hippocampal morphology and cautioned against using lifespan trajectories to infer decade-by-decade changes in the cortical surface and the hippocampus.

Medial Amygdalar Tau Is Associated With Mood Symptoms in Preclinical Alzheimer's Disease

BACKGROUND

While the amygdala receives early tau deposition in Alzheimer's disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms.

METHODS

We examined 598 individuals (347 amyloid positive [58% female], 251 amyloid negative [62% female] subset in tau positron emission tomography and functional magnetic resonance imaging cohorts) from the A4 (Anti-Amyloid Treatment in Asymptomatic AD) Study. In the tau positron emission tomography cohort, we used amygdalar segmentations to examine representative nuclei from 3 functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the functional magnetic resonance imaging cohort. Finally, we conducted exploratory post hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores.

RESULTS

Amyloid-positive individuals demonstrated increased tau binding in the medial and lateral amygdala, and tau binding in these regions was associated with mood symptoms. Across amygdalar divisions, amyloid-positive individuals had relatively higher regional connectivity from the amygdala to other temporal regions, the insula, and the orbitofrontal cortex, but medial amygdala to retrosplenial cortex connectivity was lower. Medial amygdala to retrosplenial connectivity was negatively associated with anxiety symptoms, as was retrosplenial tau.

CONCLUSIONS

Our findings suggest that preclinical tau deposition in the amygdala and associated changes in functional connectivity may be related to early mood symptoms in AD.

Neurostructural features predict binge drinking in emerging adulthood: Evidence from a 5-year follow-up study

BACKGROUND

Binge drinking (BD) involves consuming large amounts of alcohol within a short timeframe, leading to a blood alcohol concentration of 0.08g/dL or above. This pattern of alcohol consumption is prevalent among young adults and has significant implications for brain structure and subsequent drinking behaviors.

METHODS

In this prospective longitudinal study, we employed zero-inflated negative binomial regression models to examine whether various neurostructural features (i.e., volume, surface area, cortical thickness) of brain regions involved in executive and emotional/motivational processes at the age of 18-19 could predict number of BD episodes five years later, at ages 23-24, once participants were expected to complete their university degree. Specifically, we recorded magnetic resonance imaging (MRI) data from 68 students who completed both the baseline MRI and follow-up alcohol use assessment, with the aim of analyzing the predictive value of these neurostructural characteristics five years later.

RESULTS

The analysis revealed that a larger surface area in the caudal division of the right middle frontal gyrus was significantly associated with a higher incidence rate of BD episodes (IRR = 2.24, 95 % CI = 1.28-3.91, p = 0.005). Conversely, a smaller surface area in the right caudal anterior cingulate cortex was associated with a higher incidence rate of BD episodes (IRR = 0.61, 95 % CI = 0.44-0.85, p = 0.004).

CONCLUSIONS

These findings suggest that specific neurostructural characteristics during adolescence can predict BD behaviors in young adulthood. This highlights the potential of neuroimaging to identify individuals at risk for developing problematic alcohol use.

Association between large neutral amino acids and white matter hyperintensities in middle-aged adults at varying metabolic risk

This investigation delves into the interplay between large neutral amino acids (LNAA) and metabolic syndrome (MetS) in midlife adults, examining their collective influence on brain structure. While LNAA, such as tryptophan and phenylalanine, are known to bolster cognition in youth, these relationships often reverse later in life. Our study hypothesized an earlier reversal of these benefits in middle-aged adults with MetS, potentially signaling premature brain aging. Eighty participants between 40-61 years underwent MetS component quantification, LNAA measurement via high-performance liquid chromatography, and brain imaging to evaluate white matter hyperintensities (WMH) volume and medial temporal lobe (MTL) cortical thickness. Our linear regression analyses, adjusting for sex, age, and education, revealed that phenylalanine levels moderated the relationship between MetS and WMH volume (F(6, 69) = 3.134, p < 0.05, R2 = 0.214), suggesting the brain impact of MetS may be partly due to phenylalanine catabolism byproducts. LNAA metabolites did not significantly modulate the MetS-MTL cortical thickness relationship. These findings suggest that better understanding of the role of phenylalanine in the pathogenesis of cerebrovascular disease in midlife may be essential to developing early interventions to protect cognitive health. Further research is crucial to elucidate the longitudinal influence of LNAA and MetS on brain health, thereby informing strategies to mitigate cognitive decline.

APOE4 and age affect the brain entorhinal cortex structure and blood arachidonic acid and docosahexaenoic acid levels after mild TBI

A reduction in the thickness and volume of the brain entorhinal cortex (EC), together with changes in blood arachidonic acid (AA) and docosahexaenoic acid (DHA), are associated with Alzheimer's disease (AD) among apolipoprotein E ε4 carriers. Magnetic Resonance Imaging (n = 631) and plasma lipidomics (n = 181) were performed using the LIMBIC/CENC cohort to examine the influence of ε4 on AA- and DHA-lipids and EC thickness and volume in relation to mild traumatic brain injury (mTBI). Results showed that left EC thickness was higher among ε4 carriers with mTBI. Repeated mTBI (r-mTBI) was associated with reduced right EC thickness after controlling for ε4, age and sex. Age, plus mTBI chronicity were linked to increased EC White Matter Volume (WMV). After controlling for age and sex, the advancing age of ε4 carriers with blast mTBI was associated with reduced right EC Grey Matter Volume (GMV) and thickness. Among ε4 carriers, plasma tau and Aβ40 were associated with mTBI and blast mTBI, respectively. Chronic mTBI, ε4 and AA to DHA ratios in phosphatidylcholine, ethanolamides, and phosphatidylethanolamine were associated with decreased left EC GMV and WMV. Further research is needed to explore these as biomarkers for detecting AD pathology following mTBI.

A worldwide study of subcortical shape as a marker for clinical staging in Parkinson's disease

Alterations in subcortical brain regions are linked to motor and non-motor symptoms in Parkinson's disease (PD). However, associations between clinical expression and regional morphological abnormalities of the basal ganglia, thalamus, amygdala and hippocampus are not well established. We analyzed 3D T1-weighted brain MRI and clinical data from 2525 individuals with PD and 1326 controls from 22 global sources in the ENIGMA-PD consortium. We investigated disease effects using mass univariate and multivariate models on the medial thickness of 27,120 vertices of seven bilateral subcortical structures. Shape differences were observed across all Hoehn and Yahr (HY) stages, as well as correlations with motor and cognitive symptoms. Notably, we observed incrementally thinner putamen from HY1, caudate nucleus and amygdala from HY2, hippocampus, nucleus accumbens, and thalamus from HY3, and globus pallidus from HY4-5. Subregions of the thalami were thicker in HY1 and HY2. Largely congruent patterns were associated with a longer time since diagnosis and worse motor symptoms and cognitive performance. Multivariate regression revealed patterns predictive of disease stage. These cross-sectional findings provide new insights into PD subcortical degeneration by demonstrating patterns of disease stage-specific morphology, largely consistent with ongoing degeneration.

A cross-species analysis of neuroanatomical covariance sex difference in humans and mice

Structural covariance in brain anatomy is thought to reflect inter-regional sharing of developmental influences - although this hypothesis has proved hard to causally test. Here, we use neuroimaging in humans and mice to study sex-differences in anatomical covariance - asking if regions that have developed shared sex differences in volume across species also show shared sex difference in volume covariance. This study design illuminates both the biology of sex-differences and theoretical models for anatomical covariance - benefitting from tests of inter-species convergence. We find that volumetric structural covariance is stronger in adult females compared to adult males for both wild-type mice and healthy human subjects: 98% of all comparisons with statistically significant covariance sex differences in mice are female-biased, while 76% of all such comparisons are female-biased in humans (q < 0.05). In both species, a region's covariance and volumetric sex-biases have weak inverse relationships to each other: volumetrically male-biased regions contain more female-biased covariations, while volumetrically female-biased regions have more male-biased covariations (mice: r = -0.185, p = 0.002; humans: r = -0.189, p = 0.001). Our results identify a conserved tendency for females to show stronger neuroanatomical covariance than males, evident across species, which suggests that stronger structural covariance in females could be an evolutionarily conserved feature that is partially related to volumetric alterations through sex.

Deep Screening for X Chromosome Parent-of-Origin Effects on Neurobehavioral and Neuroanatomical Phenotypes in 47,XXY Klinefelter Syndrome

Background

X chromosome parent of origin (POX) has been proposed as a source of phenotypic variation within sex chromosome aneuploidies such as Klinefelter syndrome (XXY/KS) and between XX and XY individuals. However, previous studies have yielded conflicting results regarding the presence and nature of POX effects, which we sought to clarify in an expanded sample with deeper neurobehavioral phenotyping.

Methods

A cohort of 58 individuals with XXY/KS underwent duo or trio genome sequencing with parents (n = 151), measurement of 66 neurobehavioral phenotypes by standardized research assessments, and measurement of over 1000 anatomical phenotypes by structural magnetic resonance imaging. We developed a novel algorithm, the uniparental disomy visualization for variant call format files, to determine proband POX and then systematically tested for POX associations with all neurobehavioral and neuroanatomical outcomes.

Results

The uniparental disomy visualization for variant call format files algorithm showed maternal POX in 35 of 58 cases (60.3%). There were no statistically significant POX effects on any of the 66 subscale measures of cognition, psychopathology, or behavior. Neuroimaging analysis identified 2 regions in the right hemisphere with significantly higher surface area (mean effect size = 1.20) among individuals with paternal versus maternal POX (q = .021).

Conclusions

Using deeper phenotyping in an expanded sample, we did not find evidence for substantial POX effects on neurobehavioral variability, except for localized unilateral modulations of surface area in the absence of co-occurring behavioral associations. These findings help to clarify previous inconsistencies in POX research and direct attention toward other sources of clinical variability in sex chromosome aneuploidies.

Structural MRI findings in the brain related to pain distribution in chronic overlapping pain conditions: An explorative case-control study in females with fibromyalgia, temporomandibular disorder-related chronic pain and pain-free controls

BACKGROUND

Few neuroimaging studies have investigated structural brain differences associated with variations in pain distribution.

OBJECTIVE

To explore structural differences of the brain in fibromyalgia (FM), temporomandibular disorder pain (TMD) and healthy pain-free controls (CON) using structural and diffusion MRI.

METHODS

A case-control exploratory study with three study groups with different pain distribution were recruited: FM (n = 16; mean age [standard deviation]: 44 [14] years), TMD (n = 17, 39 [14] years) and CON (n = 10, 37 [14] years). Participants were recruited at the University Dental Clinic in Malmö, Sweden. T1-weighted and diffusion MRIs were acquired, clinical and psychosocial measures were obtained. Main outcome measures were subcortical volume, cortical thickness, white matter microstructure and whole brain grey matter intensity.

RESULTS

Patients with FM had smaller volume in the right thalamus than patients with TMD (p = .020) and CON (p = .030). The right thalamus volume was negatively correlated to pain intensity (r = -0.37, p = .022) and pain-related disability (r = -0.45, p = .004). The FM group had lower cortical thickness in the right anterior prefrontal cortex than CON (p = .005). Cortical thickness in this area was negatively correlated to pain intensity (r [37] = - 0.48, p = .002).

CONCLUSIONS

This study suggests that thalamus grey matter alterations are associated with FM and TMD, and that anterior prefrontal cortex grey matter alterations are associated with FM but not TMD. Studies on chronic overlapping pain conditions are needed in relation to possible nociplastic pain mechanisms in the brain and central nervous system.

APOE genotype, hippocampal volume, and cognitive reserve predict improvement by cognitive training in older adults without dementia: a randomized controlled trial

Cognitive training (CT) programs aim to improve cognitive performance and impede its decline. Thus, defining the characteristics of individuals who can benefit from these interventions is essential. Our objectives were to assess if the cognitive reserve (CR), APOE genotype (e4 carriers/non-carriers) and/or hippocampal volume might predict the effectiveness of a CT program. Participants were older adults without dementia (n = 226), randomized into parallel experimental and control groups. The assessment consisted of a neuropsychological protocol and additional data regarding total intracranial, gray matter, left/right hippocampus volume; APOE genotype; and Cognitive Reserve (CR). The intervention involved multifactorial CT (30 sessions, 90 min each), with an evaluation pre- and post-training (at six months); the control group simply following the center's routine activities. The primary outcome measures were the change in cognitive performance and the predictors of change. The results show that APOE-e4 non-carriers (79.1%) with a larger left hippocampal volume achieved better gains in semantic verbal fluency (R2 = .19). Subjects with a larger CR and a greater gray matter volume better improved their processing speed (R2 = .18). Age was correlated with the improvement in executive functions, such that older age predicts less improvement (R2 = .07). Subjects with a larger left hippocampal volume achieved more significant gains in general cognitive performance (R2 = .087). In conclusion, besides the program itself, the effectiveness of CT depends on age, biological factors like genotype and brain volume, and CR. Thus, to achieve better results through a CT, it is essential to consider the different characteristics of the participants, including genetic factors.Trial registration: Trial retrospectively registered on January 29th, 2020-(ClinicalTrials.gov -NCT04245579).

Interactive roles of preterm-birth and socioeconomic status in cortical thickness of language-related brain structures: Findings from the Adolescent Brain Cognitive Development (ABCD) study

Preterm-born (PTB) children are at an elevated risk for neurocognitive difficulties in general and language difficulties more specifically. Environmental factors such as socio-economic status (SES) play a key role for Term children's language development. SES has been shown to predict PTB children's behavioral developmental trajectories, sometimes surpassing its role for Term children. However, the role of SES in the neurocognitive basis of PTB children's language development remains uncharted. Here, we aimed to evaluate the role of SES in the neural basis of PTB children's language performance. Leveraging the Adolescent Brain Cognitive Development (ABCD) Study, the largest longitudinal study of adolescent brain development and behavior to date, we showed that prematurity status (PTB versus Term) and multiple aspects of SES additively predict variability in cortical thickness, which is in turn related to children's receptive vocabulary performance. We did not find evidence to support the differential role of environmental factors for PTB versus Term children, underscoring that environmental factors are significant contributors to development of both Term and PTB children. Taken together, our results suggest that the environmental factors influencing language development might exhibit similarities across the full spectrum of gestational age.

Atypical Brain Connectivity During Pragmatic and Semantic Language Processing in Children with Autism

BACKGROUND/OBJECTIVES

Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks.

METHODS

We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic.

RESULTS

Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere.

Network analysis of structural MRI predicts executive function in paediatric traumatic brain injury

INTRO

Paediatric traumatic brain injury (pTBI) is likely to result in cognitive impairment, specifically executive dysfunction. Evidence of the neuroanatomical correlates of this executive function (EF) impairment is derived from studies that treat morphometry of brain regions as distinct, independent features, rather than as a complex network of interrelationships. Morphometric similarity captures the meso-scale organisation of the cortex as the interrelatedness of multiple macro-architectural features and presents a novel tool with which to investigate the brain post pTBI.

METHODS

In a retrospective sample (83 pTBI patients, 33 controls), we estimate morphometric similarity from structural MRI by correlating morphometric features between cortical regions. We compared the meso-scale organisation of the cortex between groups then, using partial least squares regression, assessed the predictive validity of morphometric similarity in understanding later executive functioning, two years post-injury.

RESULTS

We found that patients and controls did not differ in terms of the overall magnitude of morphometric similarity. However, a pattern of ROI-level morphometric similarity was predictive of day-to-day EF difficulties reported by parents two years post-injury. This prediction was validated using a leave-one-out, and 20-fold cross-validation approach. Prediction was driven by regions of the prefrontal cortex, typically important for healthy maturation of EF skills in childhood. The meso-scale organisation of the cortex also produced more accurate predictions than any one morphometric feature (i.e. cortical thickness or folding index) alone.

CONCLUSION

We conclude that these methodologies show utility in predicting later executive functioning in this population.

Hypothalamus Connectivity in Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling illness of unknown etiology. Increasing evidence suggests hypothalamic involvement in ME/CFS pathophysiology, which has rarely been explored using magnetic resonance imaging (MRI) in the condition. This work aimed to use MRI to examine hypothalamus connectivity in adolescents with ME/CFS and explore how this relates to fatigue severity and illness duration. 25 adolescents with ME/CFS and 23 healthy controls completed a neuroimaging protocol consisting of structural and multishell diffusion-weighted imaging sequences, in addition to the PedsQL Multidimensional Fatigue Scale to assess fatigue severity. Information about illness duration was acquired at diagnosis. Preprocessing and streamlines tractography was performed using QSIPrep combined with a custom parcellation scheme to create structural networks. The number (degree) and weight (strength) of connections between lateralized hypothalamus regions and cortical and subcortical nodes were extracted, and relationships between connectivity measures, fatigue severity, and illness duration were performed using Bayesian regression models. We observed weak-to-moderate evidence of increased degree, but not strength, of connections from the bilateral anterior-inferior (left: pd [%] = 99.18, median [95% CI] = -22.68[-40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = -23.35[-38.47 to 8.20]), left anterior-superior (pd [%] = 99.33, median [95% CI] = -18.83[-33.45 to 4.07]) and total left hypothalamus (pd [%] = 99.44, median [95% CI] = -47.18[-83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: -0.47[-0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:-0.43[-0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group (pd [%] = 99.35, median [95% CI] = -0.28[-0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.