Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature

Mechanistic study of saltiness enhancement induced by three characteristic volatiles identified in Jinhua dry-cured ham using electroencephalography (EEG)

Excessive salt intake is a pressing food health issue, and odor-induced saltiness enhancement (OISE) is a novel strategy for targeted salt reduction. Understanding the neural mechanisms of OISE is essential for salt reduction. In this study, the mechanism of saltiness enhancement induced by three volatile organic compounds (VOCs) identified in Jinhua dry ham was investigated in 20 panelists using electroencephalography (EEG). The study demonstrated that VOCs enhanced salty taste perception, primarily through low-frequency brain waves. Source localization revealed occipital lobe activation during salty taste recognition, while OISE stimuli enhanced activity in the primary and secondary gustatory cortices. Additionally, VOCs enhanced phase synchronization among activated brain regions, as indicated by functional connectivity. This study enhances the understanding of olfactory-gustatory interactions and provides a neurological basis for the effects of OISE.

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.

Distinguishing Neural Correlates of Prediction Errors on Perceptual Content and Detection of Content

Accounting for why discrimination between different perceptual contents is not always accompanied by conscious detection of that content remains a challenge for predictive processing theories of perception. Here, we test a hypothesis that detection is supported by a distinct inference within generative models of perceptual content. We develop a novel visual perception paradigm that probes such inferences by manipulating both expectations about stimulus content (stimulus identity) and detection of content (stimulus presence). In line with model simulations, we show that both content and detection expectations influence RTs on a categorization task. By combining a no-report version of our task with functional neuroimaging, we reveal that violations of expectations (prediction errors [PEs]) about perceptual content and detection are supported by visual cortex and pFC in qualitatively different ways: Within visual cortex, activity patterns diverge only on trials with a content PE, but within these trials, further divergence is seen for detection PEs. In contrast, within pFC, activity patterns diverge only on trials with a detection PE, but within these trials, further divergence is seen for content PEs. These results suggest rich encoding of both content and detection PEs and highlight a distributed neural basis for inference on content and detection of content in the human brain.

Temporal-multimodal consistency alignment for Alzheimer's cognitive assessment prediction

BACKGROUND

As one of the most prevalent neurodegenerative disorders, Alzheimer's disease (AD) severely impacts human thinking and behavior. Early and accurate prediction of cognitive decline is crucial for timely AD intervention. However, most existing prognostic methods hardly explore the underlying association among longitudinal data from different modalities in disease progression, thus the predictive ability of current models is still quite limited.

PURPOSE

We propose the unifying Multi-Modality fusion with DUal-gRanularity Alignment framework (MM-DURA) to simultaneously model longitudinal correlations and modalities interactions for cognitive assessment forecasting. Our proposed framework leverages temporal MRI scans, time-aligned clinical diagnostics, and genomic data as inputs to forecast multiple cognitive assessment scores.

METHODS

We propose a novel coarse-to-fine feature representation learning approach to ascertain the congruence between modalities at both the subject and visit granularities. This method ensures the alignment of multimodal data pertaining to individual subjects and captures the temporal progression of these modalities. Additionally, we design a hierarchical multimodality fusion (HMF) block that can effectively exploit the interrelationships and dependencies among modalities. Lastly, we employ an LSTM-based regression head with the fused multimodality embedding as input to forecast the future status of cognitive ability.

RESULTS

We validate our method on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and investigate the optimal hierarchical structure for modality fusion. The whole dataset includes 707 subjects participating in the ADNI1, ADNIGO, and ADNI2 studies. All subjects underwent longitudinal examinations with an average study period of approximately 14 months. The subject-level split for training, validation, and testing sets is 0.75:0.05:0.20. The proposed MM-DURA framework demonstrates superior performance, achieving remarkable RMSE values of 1.099 for CDRSB, 5.601 for ADAS-Cog, 2.051 for MMSE, 6.504 for RAVLT, and 3.447 for FAQ cognitive assessments forecasting. These results outperform all six comparison methods, including two state-of-the-art multimodal temporal modeling approaches. Comprehensive ablation experimental results affirm the effectiveness of longitudinal modeling with temporal-multimodal alignment, highlighting its clinical potential for cognitive assessment prediction. Visualizations of key brain regions and SNP significance analysis also provide substantial interpretability.

CONCLUSIONS

In this work, we proposed a novel framework that unifies multimodality fusion with dual-granularity alignment for cognitive assessment forecasting. Our approach utilizes a temporal-multimodal consistency alignment strategy, which effectively synchronizes various modalities within a unified latent space. Furthermore, the innovative HMF block we developed capitalizes on the inherent relationships and dependencies between modalities to optimize data integration. Extensive numerical results on five cognitive assessment scores, supported by detailed visualizations demonstrate the superior performance of our approach compared to existing methods. Our code has been released, and it is available at https://github.com/IcecreamArtist/MM_DURA.

The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms

BACKGROUND

Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underlying biological mechanisms responsible for cortical changes in the AD cortex remain unclear.

METHODS

Fifty-eight participants with AD and 67 normal controls underwent high-resolution 3 T structural brain MRI. Using surface-based morphometry (SBM), we created vertex-wise maps for group comparisons in terms of five measures: cortical thickness, fractal dimension, gyrification index, Toro's gyrification index and sulcal depth respectively. Five machine learning (ML) models combining SBM parameters were established to predict AD. In addition, transcription-neuroimaging association analyses, as well as Mendelian randomization of AD and cortical thickness data, were conducted to investigate the genetic mechanisms and biological functions of AD.

RESULTS

AD patients exhibited topological changes in cortical complexity, with increased complexity in the frontal and temporal cortex and decreased complexity in the insula cortex, alongside extensive cortical atrophy. Combining different SBM measures could aid disease diagnosis. The genes involved in cell structure support and the immune response were the strongest contributors to cortical anatomical features in AD patients. The identified genes associated with AD cortical morphology were overexpressed or underexpressed in excitatory neurons, oligodendrocytes, and astrocytes.

CONCLUSION

Complexity alterations of the cerebral surface may be associated with a range of biological processes and molecular mechanisms, including immune responses. The present findings may contribute to a more comprehensive understanding of brain morphological patterns in AD patients.

Thalamic atrophy in multiple sclerosis is associated with tract disconnection and altered microglia

Thalamic atrophy already occurs in the early stages of multiple sclerosis (MS) and continues progressively throughout the disease. Demyelination is one of the main pathological hallmarks of MS and yet, thalamic demyelination does not correlate well with thalamic atrophy. By combining post-mortem magnetic resonance imaging with immunohistochemistry of thalami from 13 control and 13 MS donors, we investigated the underlying pathological contributors of thalamic atrophy and pathology. We first assessed the volumes of four thalamic nuclei groups (anterior, lateral, medial and posterior). Then, diffusion weighted imaging was used to assess the microstructural integrity of white matter tracts connecting each thalamic nuclei group. In addition, we studied myelination, inflammation, neurodegeneration and microglial activation by immunohistochemistry. We uncovered that medial and posterior thalamic nuclei were more atrophic compared to the anterior and lateral nuclei. Bilateral posterior nuclei and the right medial and anterior nuclei showed reduced fractional anisotropy in connected white matter tracks. We further show that microglial cells in the mediodorsal nuclei have an increased density and morphological complexity in MS compared to control donors. Microglia show signs of phagocytosis of pre-synapses, although we did not observe an overall synaptic loss in the thalamus of MS donors. These microglial changes within mediodorsal nuclei correlated with lower medial thalamic volume. Taken together, this study provides evidence that thalamic (subnuclear) atrophy relates tostructural thalamic network disconnection and within-thalamic microglial changes, but not thalamic demyelination. These findings could impact future treatment strategies aimed at thalamic neuroprotection.

Early cortical alterations and neuropsychological mechanisms in amyotrophic lateral sclerosis

OBJECTIVE

This study investigates the characteristics of cortical structural and functional alterations in amyotrophic lateral sclerosis (ALS) patients and their modulation of emotional and cognitive functions, as well as to discuss their diagnostic value in early-stage ALS.

METHODS

Fifty-nine ALS patients (28 in ALS 1 and 31 in ALS 2, categorized using King's College Staging) and 31 healthy controls were evaluated using multiparametric MRI, motor and neuropsychological assessments, and serum neurofilament light chain (NfL) levels. Mediation analyses were performed to examine how cortical alterations influence the relationship between emotional and cognitive functions. Support vector machine (SVM) classification models were constructed to assess the diagnostic utility of differential cortical parameters.

RESULTS

ALS 1 patients exhibited increased cortical thickness (CT) and functional activity in the cingulate and frontotemporal regions, correlating with neuropsychological performance and NfL levels. Mediation analysis revealed that perigenual and frontotemporal functional activity significantly modulated the relationship between depressive symptoms and cognitive function. SVM classification showed that the combined altered regions with Amplitude of Low Frequency Fluctuations (ALFF) model achieved slightly better performance (AUC = 0.853, 95 %CI: 0.687-1.000, p < 0.001) compared to CT (AUC = 0.779, 95 %CI: 0.587-0.972, p < 0.001), although both models showed limited efficacy in differentiating between ALS 1 and ALS 2 groups.

CONCLUSIONS

Cortical structural and functional alterations in ALS mediate the impact of depression on cognitive function, offering insights into the neuropsychological mechanisms of the disease and potential biomarkers for early-stage diagnosis.

Cortical myelin mapping in antipsychotic medication-naïve, first-episode psychosis patients

While white matter myelin primarily functions to accelerate conduction velocity and has been extensively studied in schizophrenia-spectrum disorders (SSD), less is known about the role of gray matter myelin in SSD. Cortical myelination occurs mostly on the proximal axons of parvalbumin positive (PV+) interneurons, where it assists in trophic support and experience-dependent plasticity. Given the role of PV+ interneuron dysfunction in SSD, it is critical to advance our understanding of cortical myelin pathology in this context. Here, we quantified myelin maps using the T1w/T2w ratio in a large group of antipsychotic medication-naïve, first-episode psychosis patients. We compared myelin content between patients (N = 91) and controls (N = 107) using a MANCOVA and calculated zero-order correlations with the discriminant function for each region, then used a machine learning approach to identify the most parsimonious constellation of cortical regions driving group differences using a stepwise algorithm. Group membership was significantly associated with T1w/T2w ratio (Wilks Lambda = 0.09, p < 0.01), where patients had higher myelin values compared to healthy controls. We identified a subset of 16 regions, primarily located in association cortices, that were sufficient to explain group differences. Here, we report an increase in the cortical T1w/T2w ratio in association cortices in first-episode psychosis. We suggest that faulty myelin compaction during this critical developmental period could contribute to PV+ interneuron pathology and cortical microcircuit disruptions resulting in the clinical phenotype. With additional empirical support from future studies, novel treatment strategies targeting cortical myelin could have potential to mitigate circuit dysfunction in the illness.

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.

Differentiation of executive functions during adolescence: Converging evidence from behavioral, genetic and neural data

Executive functions (EF) have been found to differentiate from a single component to three distinct components (i.e., updating, shifting, and inhibition) during development. However, there is still much debate regarding when such differentiation takes place and biological evidence is needed. Here we used the longitudinal and multimodality data from the ABCD study to address this question at two age groups (9-10 and 13-14). Three tasks (i.e., List, Card and Flanker tasks) were used to represent the three EF components respectively at baseline, and two tasks (Flanker and List) at 4th year follow up. Genes associated with each task were identified by whole genome and transcriptome association analyses and were then used for genetic similarity calculation; structural and functional brain indices related to each task were identified and used to assess neural similarity. We found that at baseline (9-10 years old), the three EF components were behaviorally highly inter-correlated and were associated with many of the same genes and the same brain regions. Four years later, the follow-up data (with Flanker and List tasks only) still showed significant but smaller behavioral/genetic/neural similarity. This study is the first to chart the path of EF differentiation during adolescence by combining behavioral, genetic, and neural data, and this approach may be relevant to the study of development of other cognitive abilities.

Supramodal and cross-modal representations of working memory in higher-order cortex

Working memory is essential for guiding our behaviors in daily life, where sensory information continuously flows from the external environment. While numerous studies have shown the involvement of sensory areas in maintaining working memory in a feature-specific manner, the challenge of utilizing retained sensory representations without interference from incoming stimuli of the same feature remains unresolved. To overcome this, essential information needs to be maintained dually in a form distinct from sensory representations. Here, using working memory tasks to retain braille patterns presented tactually or visually during fMRI scanning, we discovered two distinct forms of high-level working memory representations in the parietal and prefrontal cortex, together with modality-dependent sensory representations. First, we found supramodal representations in the superior parietal cortex that encoded braille identity in a consistent form, regardless of the involved sensory modality. Second, we observed that the prefrontal cortex and inferior parietal cortex specifically encoded cross-modal representations, which emerged during tasks requiring the association of information across sensory modalities, indicating a different high-level representation for integrating a broad range of sensory information. These findings suggest a framework for working memory maintenance that incorporates two distinct types of high-level representations-supramodal and cross-modal-operating alongside sensory representations.

Phase-amplitude coupling within MEG data can identify eloquent cortex

Objective.Proper identification of eloquent cortices is essential to minimize post-surgical deficits in patients undergoing resection for epilepsy and tumors. Current methods are subjective, vary across centers, and require significant expertise, underscoring the need for more objective pre-surgical mapping. Phase-amplitude coupling (PAC), the interaction between the phase of low-frequency oscillations and the amplitude of high-frequency activity, has been implicated in task-induced brain activity and may serve as a biomarker for functional mapping. Our objective was to develop a novel PAC-based algorithm to non-invasively identify somatosensory eloquent cortex using magnetoencephalography (MEG) data in epilepsy patients.Approach.We analyzed somatosensory and spontaneous MEG recordings from 30 subjects with drug-resistant epilepsy. PAC was calculated on source-reconstructed data (5-12 Hz for low frequencies and 30-300 Hz for high frequencies), followed by rank-2 tensor decomposition. Density-based clustering compared active brain regions during somatosensory task and spontaneous data at a population level. We employed a linear mixed-effects model to quantify changes in PAC between somatosensory and resting-state data. We developed a patient-specific support vector machine (SVM) classifier to identify active brain regions based on PAC values during the somatosensory task.Main results.Five of six expected brain regions were active during left and right-sided stimulation (p=1.08×10-8, hypergeometric probability test). The mixed-effects model confirmed task-specific PAC in anatomically relevant brain regions (p < 0.01). The SVM classifier gave a specificity of 99.46% and a precision of 66.9%. These results demonstrate that the PAC algorithm reliably identifies somatosensory cortex activation at both individual and population levels with statistical significance.Significance.This study demonstrates the feasibility of using PAC as a non-invasive marker for identifying functionally relevant brain regions during somatosensory task in epilepsy patients. Future work will evaluate its applicability for mapping other eloquent cortices, including language, motor, and auditory areas.

Socioeconomic deprivation, brain morphology, and body fat among children and adolescents

Given mounting literature linking environmental adversity with neurobiological alterations, other evidence has shown association between excess adiposity and attenuated brain development, leading to our current question of how the developing brain interacts with change in body composition in response to environmental challenges. Using data from the Adolescent Brain Cognitive Development (ABCD®) Study, we conducted mediation analyses and demonstrated that socioeconomic deprivation (SED) was associated with lower total brain and cortical volumes via the mediation of higher waist-to-height ratio (WHtR), and that WHtR likewise mediated the association of SED with global brain structures. The prefrontal structures showed region- and direction-specific pathways, with bilateral superior and middle frontal gyrus being most consistently related with WHtR in addition to the impact of SED. These findings reveal a functional trade-off between brain development and fat deposition in response to environmental deprivation, and may have implications for understanding neurocognitive and somatic development among children and adolescents in different socioeconomic contexts.

Naturalistic acute pain states decoded from neural and facial dynamics

Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants' high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings.

An association study of multimodal neuroimage features and gene expression in adolescents with centrally mediated abdominal pain syndrome

Centrally mediated abdominal pain syndrome (CAPS) is a complex condition characterised by persistent abdominal discomfort in the absence of clear physiological abnormalities. Its aetiology remains poorly understood, but recent research has suggested that neurobiological and genetic factors may play an important role in the pathophysiology of the syndrome. We applied partial least squares regression (PLSR) to investigate the association between multimodal neuroimaging features of 313 participants and postmortem gene expression data from AIBS. Compared to HCs, we found that (1) cortical thickness of the left inferior parietal and temporal lobes, left cingulate cortex and left caudate, sulcal depth of the right lateral occipital gyrus and volume of the right parahippocampal and right insula showed significant differences, the GO biological processes associated with these differences are mainly in "cell-substrate junction"; (2) the correlation between the default mode network and the ventral attention network, the retrosplenial temporal network and the salience network, the fronto-parietal network and the right caudate are significantly increased, the GO biological processes associated with these increases are mainly in "cell junction organization"; and (3) the mean diffusivity of sub-adjacent white matter associated with cortical ROIs of frontal cortex, cingulate cortex, temporal cortex, precuneus and insula are significantly different, the associated GO biological processes are primarily in "chromatin organization". The changed characteristics of brain neuroimaging are closely related to the biological process of down-regulation or up-regulation of gene expression. Integrating the neurobiological and genetic underpinnings is crucial to provide a theoretical framework for the mechanism of CAPS in adolescents.

Modulatory effect of levodopa on the basal ganglia-cerebellum connectivity in Parkinson's disease

Long-term levodopa use in Parkinson's disease is associated with declining efficacy and motor complications. Understanding its effects on brain reorganisation is vital for optimizing therapy. Using data from Parkinson's Progression Marker Initiative, we investigated levodopa's impact on resting-state functional connectivity within the cortico-basal ganglia-cerebellum system in 29 patients, under drug-naïve and levodopa-medicated conditions. Univariate comparisons of inter-regional connectivity between conditions were conducted, and multivariate combinations of connections within and between networks were assessed. No significant effect of levodopa was found using the univariate seed-based approach. However, the network connectivity pattern between basal ganglia and cerebellum showed robust classification power. Eigenvector Centrality Mapping (ECM) further identified functional hubs, with cerebellar hubs being the only ones within the cortico-basal ganglia-cerebellum system affected by medication. Our study provides further insight into the importance of inter-network functional connectivity in Parkinson's and the impact of levodopa medication, highlighting the often-overlooked role of the cerebellum.

Behavioral and neuroanatomical effects of soccer heading training in virtual reality: A longitudinal fMRI case study

Virtual reality (VR) technology has received considerable attention over the last few years, with applications in many performance domains including training of sports-related mental and motor skills. The exact psychological and neurobiological mechanisms underlying potential VR training effects in athletes, however, remain largely unknown. The present longitudinal functional magnetic resonance imaging (fMRI) case study reports behavioral and neuroanatomical effects of VR soccer (a.k.a. football) heading training in a male adult amateur player. The study was conducted over 8 weeks, starting with a pre-test, followed by a 4-week VR training phase, during which weekly fMRI assessments and the first behavioral post-test were conducted. After an additional 4-week retention phase, the final fMRI assessment and the second behavioral post-test were conducted. Substantial improvement in real-life heading performance was accompanied by both structural and functional neuroanatomical changes. The comparison of the T1-weighted images revealed an increase in GM volume in the left thalamus and an increase in WM volume in the bilateral cerebellum. Furthermore, the analysis of the surface images showed an increase in cortical thickness in the right insula, left inferior temporal gyrus, left parahippocampal gyrus, left lingual gyrus, left posterior cingulate cortex, and bilateral anterior cingulate and medial prefrontal cortex. The seed-based correlation analyses of the resting-state fMRI data revealed manifold increases in functional connectivity within and between important brain networks. This study contributes to the growing literature on VR training in athletes and provides the world's first evidence on fundamental neurobiological mechanisms underlying neuroplasticity related to VR training effects in sports.

Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG

Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to observe increased cortical thickness primarily in medial frontal regions, but heterogeneity across studies is common, including reports of decreased cortical thickness. These differences have not been explained. The cohort of patients investigated here originates from the Juvenile Myoclonic Epilepsy Connectome Project, which included comprehensive neuropsychological testing, 3 T MRI, and high-density 256-channel EEG. 64 JME patients aged 12-25 and 41 age and sex-matched healthy controls were included. Data-driven approaches were used to compare cortical thickness and subcortical volumes between the JME and control participants. After differences were identified, supervised machine learning was used to confirm their classification power. K-means clustering was used to generate imaging endophenotypes, which were then correlated with cognition, EEG frequency band lagged coherence from resting state high-density EEG, and white and grey matter based spatial statistics from diffusion imaging. The volumes of subcortical gray matter structures, particularly the thalamus and the motor-associated thalamic nuclei (ventral anterior), were found to be smaller in JME. In addition, the right hemisphere (primarily) sulcal pre-motor cortex was abnormally thicker in an age-dependent manner in JME with an asymmetry in the pre-motor cortical findings. These results suggested that for some patients JME may be an asymmetric disease, at least at the cortical level. Cluster analysis revealed three discrete imaging endophenotypes (left, right, symmetric). Clinically, the groups were not substantially different except for cognition, where left hemisphere disease was linked with a lower performance on a general cognitive factor ("g"). HD-EEG demonstrated statistically significant differences between imaging endophenotypes. Tract-based spatial statistics showed significant changes between endophenotypes as well. The left dominant disease group exhibited diffuse white matter changes. JME patients present with heterogeneity in underlying imaging endophenotypes that are defined by the presence and laterality of asymmetric abnormality at the level of the pre-motor sulcal cortex; these endophenotypes are linked to orderly relationships with cognition, EEG, and white matter pathology. The relationship of JME's adolescent onset, age-dependent cortical thickness loss, and seizure upon awakening all suggest that synaptic pruning may be a key element in the pathogenesis of JME. Individualized treatment approaches for neuromodulation are needed to target the most relevant cortical and subcortical structures as well as develop disease-modifying and neuroprotective strategies.

Uncovering cerebral blood flow patterns corresponding to Amyloid-beta accumulations in patients across the Alzheimer's disease continuum using the arterial spin labeling

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder ranging from mild cognitive impairment (MCI) to AD dementia. Abnormal cerebral perfusion alterations, influenced by amyloid-beta (Aβ) accumulations, have been implicated in cognitive decline along this spectrum.

OBJECTIVE

This study investigates the relationship between cerebrospinal fluid (CSF) Aβ1-42 levels and regional cerebral blood flow (CBF) changes across the AD continuum using the Arterial Spin Labeling (ASL) technique.

METHODS

We analyzed data from 229 participants extracted from the ADNI cohort, comprising of 50 cognitively normal (CN), 13 subjective memory complaints (SMC), 83 early MCI (EMCI), 52 late MCI (LMCI), and 31 AD participants with complete ASL and CSF data. Correlations between Aβ1-42 levels and regional mean CBF values were assessed. Multiple linear regression models accounted for confounders, including age, gender, and education level.

RESULTS

Preliminary unadjusted analyses revealed strong positive correlations between Aβ1-42 levels and CBF in multiple regions, predominantly in the AD group. After adjusting for confounders, significant correlations in AD participants emerged in the left pars triangularis and left caudal middle frontal cortex. In the LMCI group, significant associations were identified in the right lateral occipital cortex, right inferior parietal cortex, and left amygdala.

CONCLUSION

These findings highlight the critical role of Aβ-driven CBF alterations in regions associated with higher cognitive functions and suggest that these patterns may serve as potential biomarkers for diagnosing and monitoring disease progression.

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.

Reduced Visual-Cortex Reorganization Before and After Cochlear Implantation Relates to Better Speech Recognition Ability

Although a cochlear implant (CI) can partially restore auditory function, CI recipients show alterations not only in auditory but also in visual cortical processing. Yet, it is not well understood how these visual changes relate to the CI outcome and to what extent these changes are induced by auditory deprivation and the limited CI input, respectively. Here, we present a prospective longitudinal electroencephalography study which examined the deprivation- and CI-induced alterations on cortical face processing by comparing visual evoked potentials (VEP) in CI users before and 6 months after implantation. A group of normal-hearing (NH) listeners served as a control. The participants performed a word-identification task and a face-categorization task to study the cortical processing of static and articulating faces in attended and unattended conditions. The CI candidates and CI users showed a reduced visual-cortex activation, a stronger functional connectivity between the visual and auditory cortex, and a reduced attention effect in the (extended) alpha frequency range (8-18 Hz) when compared to NH listeners. There was a positive correlation between the P1 VEP amplitude recorded before implantation and the speech recognition ability after implantation. Our results suggest that the CI users' alterations in cortical face processing are mainly induced by auditory deprivation and not by CI experience. Importantly, these deprivation-induced changes seem to be related to the CI outcome. Our results suggest that the visual P1 amplitude as recorded before implantation provides an objective index of cortical visual reorganization that may help predict the CI outcome.

A road map to manual segmentation of cerebral structures

Manual segmentation is an essential tool in the researcher's technical arsenal. It is a frequent practice necessary for image analysis in many protocols, especially in neuroimaging and comparative brain anatomy. In the framework of emergence of studies focusing on alternative animal models, manual segmentation procedures play a critical role. Nevertheless, this critical task is often assigned to students, a process that, unfortunately, tends to be time-consuming and repetitive. Well-conducted and well-described segmentation procedures can potentially guide novice and even expert operators and enhance research works' internal and external validity, making it possible to harmonize studies and facilitate data sharing. Furthermore, recent advances in neuroimaging, such as ex vivo imaging or ultra-high-field MRI, enable new acquisition modalities and the identification of minute structures that are barely visible with typical approaches. In this context of increasingly detailed and multimodal brain studies, reflecting on methodology is relevant and necessary. Because it is crucial to implement good practices in manual segmentation per se but also in the description of the segmentation procedures in research papers, we propose a general roadmap for optimizing the technique, its process and the reporting of manual segmentation. For each of them, the relevant elements of the literature have been collected and cited. The article is accompanied by a checklist that the reader can use to verify that the critical steps are being followed.

Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset. However, most existing methods face challenges in: 1) mining the brain network-specific topological structure and addressing the single nucleotide polymorphisms (SNPs)-related noise contamination and 2) extracting the discriminative properties of brain imaging genomics, resulting in limited accuracy for MCI diagnosis. To this end, a modality-aware discriminative fusion network (MA-DFN) is proposed to integrate the complementary information from brain imaging genomics to diagnose MCI. Specifically, we first design two modality-specific feature extraction modules: the graph convolutional network with edge-augmented self-attention module (GCN-EASA) and the deep adversarial denoising autoencoder module (DAD-AE), to capture the topological structure of brain networks and the intrinsic distribution of SNPs. Subsequently, a discriminative-enhanced fusion network with correlation regularization module (DFN-CorrReg) is employed to enhance inter-modal consistency and between-class discrimination in brain imaging and genomics. Compared to other state-of-the-art approaches, MA-DFN not only exhibits superior performance in stratifying cognitive normal (CN) and MCI individuals but also identifies disease-related brain regions and risk SNPs locus, which hold potential as putative biomarkers for MCI diagnosis.

Cognition-Associated Changes in Retinal Thickness Relate to Limbic and Temporal Cortical Atrophy in Parkinson's Disease

BACKGROUND

Research links retinal changes to cognitive decline in Parkinson's disease (PD), paralleling findings in Alzheimer's, raising questions about specific cortical patterns of cognition-related retinal abnormalities in PD.

OBJECTIVE

The study aimed to explore whether retinal thinning linked to cognitive decline could act as a potential biomarker for cerebral atrophy in PD.

METHODS

Twenty seven patients with PD underwent cognitive and neurological assessments, along with retinal imaging using OCT and cerebral imaging using structural MRI. After identifying abnormal retinal layers associated with cognitive dysfunction through partial correlation analyses controlling for age-related effects, associations between these retinal layers and the parcellated cerebral gray matter were assessed using multiple comparison-corrected partial correlation analyses adjusted for age and gender.

RESULTS

Significant positive correlations were found between cognitive impairment measured by MoCA and specific retinal layers (IPL, GCL, and RNFL). Of these, strong associations were observed between the IPL and GCL and cortical thickness in brain the temporal lobe and limbic cortex, with more detailed further analysis showing significant correlations particularly within the middle and posterior cingulate cortex in the limbic cortex and the middle and superior temporal gyrus in the temporal lobe.

CONCLUSION

Correlations between retinal thinning, cognitive decline, and specific patterns of cortical atrophy in PD support a potential of retinal measurements as a biomarker for cognitive impairment linked to cerebral neurodegeneration.

Cortical Structure in Nodes of the Default Mode Network Estimates General Intelligence

INTRODUCTION

A growing number of studies implicate functional brain networks in intelligence, but it is unclear if network nodal structure relates to intelligence.

METHODS

Using MRI, we studied the relationship of the general intelligence factor (g) with cortical thickness (CT), local gyrification index (LGI), and voxel-based morphometry in the nodes of the default mode network (DMN) and task-positive network (TPN) in a cohort of 44 young, healthy adults. Employing a novel strategy, we performed repeated analyses with multiple sets of g estimates to remove false positives.

RESULTS

CT and LGI in medial and temporal nodes of the DMN were reliably correlated with g (p < 0.05; Pearson's coefficient: ‑0.52 to ‑0.25 and 0.22 to 0.41, respectively). Linear regression models were developed with these parameters to estimate individual g scores, with a median adj. R2 of 0.25.

CONCLUSION

Cortical thickness and gyrification in key nodes of the Default Mode Network correlate with intelligence. Linear regression models with these cortical parameters may provide an estimate of the g factor.

Adversarial testing of global neuronal workspace and integrated information theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

Tactile contribution extends beyond exteroception during spatially guided finger movements

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand movements when in contact with a surface, we predicted poorer performance when the participants traced with their bare finger compared to when their tactile sensation was dampened by a smooth, rigid finger splint. The results supported this prediction. EEG source analyses revealed smaller current in the source-localized somatosensory cortex during sensory conflict when the finger directly touched the surface. This finding supports the hypothesis that, in response to mirror-reversed visual feedback, the central nervous system selectively gated task-irrelevant somatosensory inputs, thereby mitigating, though not entirely resolving, the visuo-somatosensory conflict. Together, our results emphasize touch's involvement in movement control over a surface, challenging the notion that vision predominantly governs goal-directed hand or finger movements.

Direct brain recordings reveal implicit encoding of structure in random auditory streams

The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

Reducing Tinnitus via Inhibitory Influence of the Sensorimotor System on Auditory Cortical Activity

Tinnitus is the subjective perception of a sound without corresponding external acoustic stimuli. Research highlights the influence of the sensorimotor system on tinnitus perception. Associated neuronal processes, however, are insufficiently understood, and it remains unclear how and at which hierarchical level the sensorimotor system interacts with the tinnitus-processing auditory system. We therefore asked 23 patients suffering from chronic tinnitus (11 males) to perform specific exercises, aimed at relaxing or tensing the jaw area, which temporarily modulated tinnitus perception. Associated neuronal processes were assessed using magnetencephalography. Results show that chronic tinnitus patients experienced their tinnitus as weaker and less annoying after completion of relaxing compared with tensing exercises. Furthermore, (1) sensorimotor alpha power and alpha-band connectivity directed from the somatosensory to the auditory cortex increased and (2) gamma power in the auditory cortex reduced, which (3) related to reduced tinnitus annoyance perception on a trial-by-trial basis in the relaxed state. No effects were revealed for 23 control participants without tinnitus (six males) performing the same experiment. We conclude that the increase in directed alpha-band connectivity from the somatosensory to the auditory cortex most likely reflects the transmission of inhibition from the somatosensory to the auditory cortex during relaxation, where concurrently tinnitus-related gamma power reduces. We suggest that revealed neuronal processes are transferable to other tinnitus-modulating systems beyond the sensorimotor one that is involved in attentional or emotional tinnitus modulation and provides deeper mechanistic insights into how and through which channels phantom sound perception might be modulated on a neuronal level.

An Exploration on Aperiodic Activities and Transient Oscillations During Semantic Processing: A Study With Wearable MEG

The processing of semantic information is pivotal in language cognition. However, there is a scarcity of research exploring the semantic-related patterns associated with aperiodic and transient periodic brain activities. In this study, recently developed algorithms were employed to parameterize the time-frequency characteristics of neural activities captured with optically pumped magnetometers-based wearable Magnetoencephalography from participants engaged in a Chinese semantic-based task. This study elucidated the neural mechanisms during semantic processing, in relation to transient oscillations and aperiodic activity. Additionally, the results demonstrated that these parameterized features could serve as indicators for decoding semantics. These findings may offer novel contribution to analyzing the mechanism of semantic perception, which will be potential to rehabilitation of language disorders with OPM-MEG.

Machine learning-derived multimodal Neurobiological profiles of behavioral activation traits in adolescents

Behavioral activation (BA) traits mediate responses to positive reinforcement, and then to promote reward-seeking actions. However, few studies have investigated the neurobiological profiles of BA traits in adolescents based on multimodal neuroimaging and machine learning techniques. In this study, a total of 6626 adolescents with both valid multimodal magnetic resonance imaging (MRI) and questionnaire data were included in the Adolescent Brain Cognitive Development Study. Machine learning-based elastic net regression with 5-fold cross-validation (CV) was used to characterize the neurobiological profiles of BA traits using multimodal MRI data as predictors. Using 5-fold CV, the multi-region neurobiological profiles substantively predicted BA traits, and this finding was robust in an out-of-sample. Regarding specific regions, neurobiological profiles were enriched in the bilateral pallidum. Regarding functional networks, functional connectivity of the cingulo-opercular and the fronto-parietal networks with both the pallidum and nucleus accumbens, showed high beta weights. The relationships of the neurobiological profiles with BA traits were further supported by traditional univariate linear mixed effects models, in which many of the profiles identified as part of the neurobiological pattern showed significant univariate associations with BA traits, including the hub region pallidum. In summary, these findings revealed robust machine learning-derived neurobiological profiles of BA traits, those that comprised a key node the pallidum, which is involved in the motivational brain network. These findings suggested that the pallidum might play a vital role in developing BA traits in adolescents.

Differential brainstem connectivity according to sex and menopausal status in healthy male and female individuals

BACKGROUND

Brainstem nuclei play a critical role in both ascending monoaminergic modulation of cortical function and arousal, and in descending bulbospinal pain modulation. Even though sex-related differences in the function of both systems have been reported in animal models, a complete understanding of sex differences, as well as menopausal effects, in brainstem connectivity in humans is lacking. This study evaluated resting-state connectivity of the dorsal raphe nucleus, right and left locus coeruleus complex (LCC), and periaqueductal gray (PAG) according to sex and menopausal status in healthy individuals. In addition, relationships between systemic estrogen levels and brainstem-network connectivity were examined in a subset of participants.

METHODS

Resting-state fMRI was performed in 47 healthy male (age, 31.2 ± 8.0 years), 53 healthy premenopausal female (age, 24.7 ± 7.3 years; 22 in the follicular phase, 31 in the luteal phase), and 20 postmenopausal female participants (age, 54.6 ± 7.2 years). Permutation Analysis of Linear Models (5000 permutations) was used to evaluate differences in brainstem-network connectivity according to sex and menopausal status, controlling for age. In 10 males and 17 females (9 premenopausal; 8 postmenopausal), estrogen and estrogen metabolite levels in plasma and stool were determined by liquid chromatography-mass spectrometry/mass spectrometry. Relationships between estrogen levels and brainstem-network connectivity were evaluated by partial least squares analysis.

RESULTS

Left LCC-executive control network connectivity showed an overall sex difference (p = 0.02), with higher connectivity in females than in males; however, this was mainly due to differences between males and premenopausal females (p = 0.008). Additional sex differences were dependent on menopausal status: PAG-default mode network (DMN) connectivity was higher in postmenopausal females than in males (p = 0.04), and PAG-sensorimotor network (SMN) connectivity was higher in premenopausal females than in males (p = 0.03) and postmenopausal females (p = 0.007). Notably, higher free 2-hydroxyestrone levels in stool were reliably associated with higher PAG-SMN and PAG-DMN connectivity in premenopausal females (p < 0.01).

CONCLUSIONS

Healthy females show higher brainstem-network connectivity involved in cognitive control, sensorimotor function, and self-relevant processes than males, dependent on their menopausal status. Further, 2-hydroxyestrone, implicated in pain, may modulate PAG connectivity in premenopausal females. These findings may relate to differential vulnerabilities to chronic stress-sensitive disorders at different life stages.

Anatomical abnormalities suggest a compensatory role of the cerebellum in early Parkinson's disease

Brain atrophy is detected in early Parkinson's disease (PD) and accelerates over the first few years post-diagnosis. This was captured by multiple cross-sectional studies and a few longitudinal studies in early PD. Yet only a longitudinal study with a control group can capture accelerated atrophy in early PD and differentiate it from healthy ageing. Accordingly, we performed a multicohort longitudinal analysis between PD and healthy ageing, examining subcortical regions implicated in PD pathology, including the basal ganglia, thalamus, corpus callosum (CC), and cerebellum. Longitudinal volumetric analysis was performed on 56 early PD patients and 53 matched controls, with scans collected 2-3 years apart. At baseline, the PD group showed a greater volume in the pallidum, thalamus, and cerebellar white matter (WM), suggesting potential compensatory mechanisms in prodromal and early PD. After 2-3 years, accelerated atrophy in PD was observed in the putamen and cerebellar WM. Interestingly, healthy controls - but not PD patients - demonstrated a significant decline in Total Intracranial Volume (TIV), and atrophy in the thalamus and mid-CC. Between-group analysis revealed more severe atrophy in the right striatum and cerebellar WM in PD, and in the mid-posterior CC in controls. Using CEREbellum Segmentation (CERES) for lobule segmentation on the longitudinal PD cohort, we found a significant decline in the WM of non-motor regions in the cerebellum, specifically Crus I and lobule IX. Our results highlight an initial increase in cerebellar WM volume during prodromal PD, followed by significant degeneration over the first few years post-diagnosis.

The analyses of structural covariance and structural covariance similarity of cortical morphological measures

Structural covariance refers to the concurrent changes in one morphological measure between two brain regions. Structural covariance of cortical morphological measures such as cortical thickness (CT), surface area (SA), and cortical volume (CV) have been applied to identify brain structural differences between patients with neuropsychiatric disorders and healthy controls. However, the precise relationships between structural covariance patterns of different cortical measures remain largely unknown. Here, we optimized the preprocessing and calculation approaches of structural covariances and investigated both global (whole-brain-level) and regional (brain-region-level) structural covariance similarities between CT, SA, and CV in 35,580 individuals. We found that Pearson correlation outperformed partial correlation due to generating fewer negative correlations of uncertain biological significance and principal component regression outperformed the regressions of total intracranial volume and respective global measures in removing global effects and reducing negative correlations. We observed that both global and regional covariance similarities of SA-CV were much higher than those of CT-CV and CT-SA, although they were influenced by the selection of atlases and covariance values. We also found age and sex effects on structural covariances and age effects on covariance similarities. The higher SA-CV covariance similarities than CT-CV indicates that SA contributes more to CV covariance than CT, although CV is derived from both CT and SA. The lack of CT-SA covariance similarities suggests that CT and SA have different covariance patterns and should be used in combination in structural covariance studies.

Self-reported concussion history is not related to cortical volume in college athletes

The long-term consequences of concussion are still being uncovered but have been linked to disruptions in cognition and psychological well-being. Previous studies focusing on the association between concussion history and structural changes in the brain have reported inconsistent results. We sought to examine the effect of concussion history on cortical volume with a focus on functional networks. These networks are associated with many of the functions that can be disrupted in those with an extensive concussion history. We collected baseline behavioral data including the Immediate Post-Concussion Assessment and Cognitive Testing, a self-report measure of the number of diagnosed concussions, and structural MRI in college athletes (n=296; 263 men and 33 women, age range 17-24). Behavioral measures were collected by members of the Department of Athletics concussion management team using a standardized protocol as part of their on-boarding process. Collegiate athletes in the present study who self-reported concussion history did not report different baseline symptoms and did not exhibit consistent differences in cognitive performance relative to those who reported no concussion history. We found that concussion history was not related to cortical volume at the network or region level, even when we compared participants with two or more concussions to those with no concussion history. We did identify relationships between cortical volume in the visual network and dorsal attention network with cognitive performance. In addition to comparing cortical volume between individuals with and without reported concussion history, we also examined whether cortical volume changes could be observed within individuals from baseline to acutely following concussion. We found that network level cortical volume did not change within subjects from baseline measurement to acutely post-concussion. Together, these results suggest that both self-reported concussion history and acute concussion effects are not associated with changes in cortical volume in young adult athletes.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

A multiscale model to explain the spatiotemporal progression of amyloid beta and tau pathology in Alzheimer's disease

Amyloid-beta (Aβ) and tubulin-associated unit (tau) proteins are key biomarkers of Alzheimer's disease (AD), detectable by Positron Emission Tomography (PET) imaging and Cerebrospinal Fluid (CSF) assays. They reflect insoluble fibrils in the brain and soluble monomers in the cerebrospinal fluid, respectively. PET and CSF biomarkers have been utilized in diagnosing AD; however, their incomplete agreement significantly confounds the early detection. Additionally, the molecular mechanisms underlying the dynamics of AD biomarkers remain elusive and are yet to be quantitatively revealed. To answer these questions, we develop a multiscale mathematical model that characterizes various forms of AD biomarkers, including soluble molecules in cerebrospinal fluid, diffusive biomarkers across brain regions, and insoluble fibrils in the brain. Mathematical modeling of soluble and insoluble molecules enables the explanation of the asynchronous trajectory of AD biomarkers. Our model captures the spatiotemporal dynamics of Aβ and tau with neurodegeneration in AD. Simulation results demonstrate that the PET-CSF discordance is a typical stage in the natural history of protein aggregation, with CSF becoming abnormal before the onset of PET abnormality. Furthermore, correlation analysis reveals that neurodegeneration is more strongly associated with tau-PET than Aβ-PET. These findings suggest CSF Aβ is recognized as a biomarker at the early stage of AD, while tau-PET is more suitable for neurodegeneration assessment. The proposed multiscale model explains the underlying neurobiological factors contributing to neurodegeneration and offers a valuable tool for improving early detection and treatment strategies in clinical trials.

Isoflavone intake is associated with longitudinal changes in hippocampal volume, but not total grey matter volume, in Japanese middle-aged and older community dwellers

PURPOSE

This study aimed to clarify the associations between isoflavone intake and the volume changes of brain regions, specifically the hippocampus and total grey matter (TGM), over 10 years in Japanese middle-aged and older community dwellers.

METHODS

Data from the National Institute for Longevity Sciences-Longitudinal Study of Aging of 654 men and 671 women aged 40-85 years at baseline (6th wave survey, 2008-2010) who participated in the follow-up study (9th wave survey, 2018-2022) were analyzed. Total isoflavone intake was estimated based on a 3-day dietary record and categorized into quintile groups. The volumes of the hippocampus and TGM were measured by T1-weighted magnetic resonance imaging and longitudinal FreeSurfer software. Estimated mean brain volume changes by quintile of total isoflavone intake were assessed by a general linear model, with a stratified analysis by age group (< 65/≥65 years).

RESULTS

There were no significant associations between quintile of isoflavone intake and both brain volume changes over 10 years in all participants. On stratification by age group, the multivariable-adjusted difference over time and % change in hippocampal volume were more strongly associated with quintile of total isoflavone intake in those aged ≥ 65 years (difference over time: p for trend = 0.009; % change: p for trend = 0.012). There were no significant longitudinal associations between quintile of total isoflavone intake and TGM volume change in both age groups.

CONCLUSION

In older Japanese people aged ≥ 65 years, increased intake of total isoflavones might be a new nutritional strategy to prevent hippocampal atrophy.

Spike-phase coupling of subthalamic neurons to posterior perisylvian cortex predicts speech sound accuracy

Speech provides a rich context for understanding how cortical interactions with the basal ganglia contribute to unique human behaviors, but opportunities for direct human intracranial recordings across cortical-basal ganglia networks are rare. Here we have recorded electrocorticographic signals in the cortex synchronously with single units in the basal ganglia during awake neurosurgeries where participants spoke syllable repetitions. We have discovered that individual subthalamic nucleus (STN) neurons have transient (200 ms) spike-phase coupling (SPC) events with multiple cortical regions. The spike timing of STN neurons is locked to the phase of theta-alpha oscillations in the supramarginal and posterior superior temporal gyrus during speech planning and production. Speech sound errors occur when this STN-cortical interaction is delayed. Our results suggest that timely interactions between the STN and the posterior perisylvian cortex support auditory-motor coordinate transformation or phonological working memory during speech planning. These findings establish a framework for understanding cortical-basal ganglia interaction in other human behaviors, and additionally indicate that firing-rate based models are insufficient for explaining basal ganglia circuit behavior.

Modulating limbic circuits in temporal lobe epilepsy: impacts on seizures, memory, mood and sleep

Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures that often originate within limbic networks involving amygdala and hippocampus. The limbic network is involved in crucial physiologic functions involving memory, emotion and sleep. Temporal lobe epilepsy is frequently drug-resistant, and people often experience comorbidities related to memory, mood and sleep. Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is an established therapy for temporal lobe epilepsy. However, the optimal stimulation parameters and their impact on memory, mood and sleep comorbidities remain unclear. We used an investigational brain sensing-stimulation implanted device to accurately track seizures, interictal epileptiform spikes (IES), and memory, mood and sleep comorbidities in five ambulatory subjects. Wireless streaming of limbic network local field potentials (LFPs) and subject behaviour were captured on a mobile device integrated with a cloud environment. Automated algorithms applied to the continuous LFPs were used to accurately cataloged seizures, IES and sleep-wake brain state. Memory and mood assessments were remotely administered to densely sample cognitive and behavioural response during ANT-DBS in ambulatory subjects living in their natural home environment. We evaluated the effect of continuous low-frequency and duty cycle high-frequency ANT-DBS on epileptiform activity and memory, mood and sleep comorbidities. Both low-frequency and high-frequency ANT-DBS paradigms reduced seizures. However, continuous low-frequency ANT-DBS showed greater reductions in IES, electrographic seizures and better sleep and memory outcomes. These results highlight the potential of synchronized brain sensing and dense behavioural tracking during ANT-DBS for optimizing neuromodulation therapy. While studies with larger patient numbers are needed to validate the benefits of low-frequency ANT-DBS, these findings are potentially translatable to individuals currently implanted with ANT-DBS systems.

Individual-level deviations from normative brain morphology in violence, psychosis, and psychopathy

Neuroimaging research has shown brain morphological abnormalities associated with violence and psychosis, but individual differences are substantial and results not consistent across studies. Normative modeling of brain MRI-features facilitates a systematic mapping of individual brain characteristics of complex phenotypes also in small samples but has not yet been applied to forensic psychiatry populations. We explored brain heterogeneity in persons with a history of severe violence with a comorbid schizophrenia spectrum disorder (SSD-V; n = 38), non-violent persons with schizophrenia spectrum disorders (SSD-NV; n = 138), persons with a history of severe violence without comorbid schizophrenia spectrum disorder (nonSSD-V; n = 20), and healthy non-violent participants (HC; n = 196) from lifetime normative trajectories of cortical thickness, surface area, and subcortical volumes. Normative models based on Freesurfer derived regions of interest from 58,836 individuals were used to investigate individual deviances, group differences, and associations to psychopathy traits. We found overall heterogeneous patterns of individual deviations from the norm, which were most prominent for regions within the collateral transverse sulcus, lingual gyrus, and cerebellum among SSD-V, a pattern that differed from SSD-NV (parieto-occipital and suborbital sulci), and nonSSD-V (paracentral and middle frontal regions). We found no significant associations to psychopathy traits. By applying normative modeling, we demonstrate heterogeneous patterns of brain morphometry deviations associated with violence and psychosis. While the results warrant replication, studies addressing individual brain deviations may contribute to improved understanding of the neurobiological underpinnings of comorbid violence and psychosis, which ultimately may have clinical impact on treatment and forensic psychiatric evaluations.

Neural activity is modulated by spontaneous and volitionally controlled breathing

Recent studies have provided evidence regarding respiration-brain coupling, but our understanding of how continuously varying dynamics of breathing modulate neural activity remains incomplete. We examined whether the neural state differs between spontaneous and volitionally controlled breathing and across the phases of breathing, inspiration and expiration. Magnetoencephalography (MEG) with a respiratory belt was used to record cortical oscillatory activity during spontaneous, deep, and square breathing (n = 33). Additionally, self-report measures of mood and arousal were applied to assess changes in the psychological state during the breathing techniques. Alpha power was suppressed during inspiration and increased during expiration (p < .01) indicating dynamically fluctuating neural states across the respiratory cycle. This effect was observed in the sensorimotor areas during both spontaneous and volitionally controlled deep breathing. Compared to spontaneous and volitionally controlled square breathing, alpha power increased during deep breathing (p < .01) within a cortical network extending to frontal and temporal areas. We also observed a steeper aperiodic slope and a broadband shift in the power spectrum in the left superior frontal gyrus during square breathing in comparison with spontaneous breathing suggesting that not only oscillatory activity but also the more general spectral characteristics of ongoing neural activity are modulated by the rate, depth, and pattern of breathing. Self-reported mood and arousal did not differ across the breathing techniques. Altogether, we demonstrate that neural activity is modulated by the phases of breathing and can also be volitionally influenced by varying the rate, depth, and pattern of breathing.

Quantitative magnetic resonance imaging in Alzheimer's disease: a narrative review

Background and Objective

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by progressive cognitive decline and is traditionally associated with grey matter pathology. Recent research highlights the significance of white matter and myelin damage in AD, presenting a paradigm shift in understanding the disease. The aim of this study was to summarize current advancements in magnetic resonance imaging (MRI) techniques and their applications in assessing myelin and brain pathology in AD with a special focus on ultrashort echo time (UTE) based techniques, alongside the role of artificial intelligence (AI) in enhancing diagnostic accuracy.

Methods

Between April and May 2024, we conducted a literature search using Google Scholar, Web of Science, and PubMed, focusing on publications from 1990 to 2024. Search terms included "Quantitative imaging", "Alzheimer's MRI", "T1ρ Alzheimer's", "MT imaging Alzheimer's", and "myelin water fraction Alzheimer's". We included quantitative MRI studies involving AD brains and excluded volumetric analyses, non-quantitative studies, non-English reports, non-peer-reviewed studies, and animal research.

Key Content and Findings

Quantitative MRI techniques, including T1, T1ρ, magnetization transfer ratio (MTR), T2, T2*, susceptibility, myelin water fraction (MWF), and non-aqueous myelin proton density (PD) were described. These biomarkers represent different pathophysiological elements of brain damage and may have distinct functions at different phases of the disease. The role of AI in enhancing diagnostic accuracy is also discussed.

Conclusions

In conclusion, integrating advanced MRI techniques and AI offers promising avenues for understanding and diagnosing AD. The focus on myelin damage and white matter integrity underscores the importance of comprehensive imaging approaches. Continued research and development are essential to address current challenges and improve clinical practice in AD diagnostics.

Maintaining Brain Health: The Impact of Physical Activity and Fitness on the Aging Brain-A UK Biobank Study

The growing prevalence of physical and neurological disorders linked to aging poses significant challenges for society. Many of these disorders are closely linked to changes in brain structure and function, highlighting the importance of identifying protective factors that can preserve brain structure in later life and mitigate age-related decline. Physical activity (PA) is consistently linked to physical health and was found to mitigate age-related disorders. However, its effects on markers of brain aging remain inconclusive, partly due to reliance on underpowered studies and self-reported data. We investigated the effects of accelerometer-measured PA and physical fitness on BrainAGE, a machine-learning-derived marker of brain aging, in a large UK Biobank cohort. Using cortical and subcortical neuroimaging-derived features, a BrainAGE model was trained on 21,442 participants (mean absolute error: 3.75 years) and applied to predict BrainAGE for an independent sample of 10,874 participants. Accelerometer-measured moderate-intensity PA, but not self-reported PA, was associated with decelerated brain aging, indicated by a negative BrainAGE. Further, higher hand grip strength, along with lower body mass index (BMI), diastolic blood pressure (DBP), and resting heart rate, was linked to decelerated aging. These fitness measures impacted BrainAGE independently of PA. Additionally, fitness partially accounted for the relationship between PA and BrainAGE. Specifically, BMI, DBP, and resting heart rate showed a significant mediating effect, while grip strength did not. These findings highlight the interplay between PA and fitness in maintaining brain health and provide valuable insights for neuroscience and preventive health measures.

Cardiac dysfunction is associated with indices of brain atrophy and cognitive impairment in heart failure with reduced ejection fraction

Cardiac dysfunction in heart failure with reduced ejection fraction (HFrEF) may contribute to brain atrophy and cognitive decline beyond that which is typical of healthy aging. This study tested the hypothesis that HFrEF would be associated with regionally unique brain remodeling and impaired cognitive performance independent of age. Furthermore, that cardiac index and clinical markers of HFrEF severity would predict brain remodeling and cognition with age and HFrEF, respectively. Cardiac function and brain morphology were assessed using magnetic resonance imaging in young healthy adults (24 ± 6 yr), older healthy adults (60 ± 6 yr), and patients living with HFrEF (59 ± 6 yr). The Montreal Cognitive Assessment was administered to assess cognition. Gray matter volume (GMV) (young: 492 ± 24, old: 456 ± 24, HFrEF: 433 ± 32 cm3, P ≤ 0.05) and cortical thickness (young: 2.44 ± 0.07, old: 2.33 ± 0.08, HFrEF: 2.22 ± 0.10 mm, P < 0.01) were lower with age and lowered further with HFrEF. Regional analysis revealed a unique pattern of atrophy with HFrEF. Whereas age had little effect on cortical curvature (P = 0.60), it was greater in HFrEF (young: 0.127 ± 0.003, old: 0.128 ± 0.003, HFrEF: 0.136 ± 0.005 mm-1, P < 0.01). Cardiac index was the best correlate of brain atrophy and cognitive performance with age (R = 0.33-0.47; P < 0.05). However, EF and end systolic volume index were better correlates of brain atrophy and cognitive performance in HFrEF (R = -0.50-0.49; P ≤ 0.05). These data indicate that lower GMV and cortical thickness in HFrEF are not merely an acceleration of age-related declines but reflect a unique pattern of brain atrophy and remodeling. In addition, classic markers of HF severity may be better predictors of pathological brain remodeling than reduced cardiac index.NEW & NOTEWORTHY Lower gray matter volume, and cortical thinning in heart failure are regionally dependent, and independent of age. Patients living with heart failure had higher cortical curvature, but older adults did not. Lower gray matter volume, cortical thinning, and cognitive impairment were associated with markers of cardiac dysfunction, with ejection fraction, and end systolic volume index being better predictors among the older and heart failure cohort than cardiac index.

Romer-EPTI: Rotating-view motion-robust super-resolution EPTI for SNR-efficient distortion-free in-vivo mesoscale diffusion MRI and microstructure imaging

PURPOSE

To overcome the major challenges in diffusion MRI (dMRI) acquisition, including limited SNR, distortion/blurring, and susceptibility to motion artifacts.

THEORY AND METHODS

A novel Romer-EPTI technique is developed to achieve SNR-efficient acquisition while providing distortion-free imaging, minimal spatial blurring, high motion robustness, and simultaneous multi-TE imaging. It introduces a ROtating-view Motion-robust supEr-Resolution technique (Romer) combined with a distortion/blurring-free Echo Planar Time-resolved Imaging (EPTI) readout. Romer enhances SNR through simultaneous multi-thick-slice acquisition with rotating-view encoding, while providing high motion-robustness via a high-fidelity, motion-aware super-resolution reconstruction. Instead of EPI, the in-plane encoding is performed using EPTI readout to prevent geometric distortion, T2/T2*-blurring, and importantly, dynamic distortions that could introduce additional blurring/artifacts after super-resolution reconstruction due to combining volumes with inconsistent geometries. This further improves effective spatial resolution and motion robustness. Additional developments include strategies to address slab-boundary artifacts, achieve minimized TE and optimized readout for additional SNR gain, and increase robustness to strong phase variations at high b-values.

RESULTS

Using Romer-EPTI, we demonstrated distortion-free whole-brain mesoscale in-vivo dMRI at both 3T (500-μm isotropic [iso] resolution) and 7T (485-μm iso resolution) for the first time. Motion experiments demonstrated the technique's motion robustness and its ability to obtain high-resolution diffusion images in the presence of subject motion. Romer-EPTI also demonstrated high SNR gain and robustness in high b-value (b = 5000 s/mm2) and time-dependent dMRI.

CONCLUSION

The high SNR efficiency, improved image quality, and motion robustness of Romer-EPTI make it a highly efficient acquisition for high-resolution dMRI and microstructure imaging.

Reduced recruitment of inhibitory control regions in very young children with ADHD during a modified Kiddie Continuous Performance Task: A fMRI study

Attention-Deficit/Hyperactivity Disorder (ADHD) symptom profiles are known to undergo changes throughout development, rendering the neurobiological assessment of ADHD challenging across different developmental stages. Particularly in young children (ages 4- to 7-years), measuring inhibitory control network activity in the brain has been a formidable task due to the lack of child-friendly functional Magnetic Resonance Imaging (fMRI) paradigms. This study aims to address these difficulties by focusing on measuring inhibitory control in very young children within the MRI environment. A total of 56 children diagnosed with ADHD and 78 typically developing (TD) 4-7-year-old children were successfully examined using a modified version of the Kiddie-Continuous Performance Test (K-CPT) during BOLD fMRI to assess inhibitory control. We also evaluated their performance on the standardized K-CPT outside the MRI scanner. Our findings suggest that the modified K-CPT effectively elicited robust and expected brain activity related to inhibitory control in both groups who were successfully scanned. Comparisons between the two groups revealed differences in brain activity, primarily observed in inferior frontal gyrus, anterior insula, dorsal striatum, medial pre-supplementary motor area (pre-SMA), and cingulate cortex (p < .005, corrected). Notably, for both groups increased activity in the right anterior insula was associated with improved response time (RT) and reduced RT variability on the K-CPT administered outside the MRI environment, although this did not survive statistical correction for multiple comparisons. The study also revealed continuing challenges for scanning this population-an additional 51 TD children and 78 children with ADHD were scanned, but failed to provide useable data due to movement. In summary, for a subsample of children, we successfully overcame some of the challenges of measuring inhibitory control in very young children within the MRI environment by using a modified K-CPT during BOLD fMRI, but further challenges remain for scanning in this population. The findings shed light on the neurobiological correlates of inhibitory control in ADHD and TD children, provide valuable insights for understanding ADHD across development, and potentially inform ADHD diagnosis and intervention strategies. The research also highlights remaining challenges with task fMRI in very young clinical samples.

A simple clustering approach to map the human brain's cortical semantic network organization during task

Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

Effects of manual therapy combined with therapeutic exercise on brain structure in patients with chronic nonspecific neck pain: A randomized controlled trial

This trial aimed to investigate the effects of 10-week manual therapy combined with exercise compared to routine physical therapy on brain structure and clinical outcomes in patients with neck pain. Fifty-two participants with chronic nonspecific neck pain were randomized into either an intervention group or a control group (a 1:1 ratio). The intervention group received cervical mobilization and cervical and scapular exercises. The control group received routine physical therapy. The primary outcomes were cortical thickness and volume. Secondary outcomes were neck pain intensity, disability, psychological symptoms, cervical range of motion and cervical flexor muscle strength. Outcome measures were taken at baseline and post-treatment. There was no loss to follow-up. Compared to baseline, significant differences in cortical thickness were observed at post-treatment in both groups, including prefrontal cortex (PFC), anterior cingulate cortex (ACC), primary somatosensory cortex (S1), primary motor cortex (M1) and precuneus (p<0.05). The intervention group exhibited greater increases in cortical thickness in the ACC and M1 compared to controls (p<0.05). The secondary outcomes were improved in both groups (p<0.05). There were differences in brain structure (S1, PFC and insula) between participants who experienced ≥50% reduction in pain intensity and those with <50% reduction (p<0.05). Changes in brain structure were correlated with changes in pain intensity and neck disability (r =-0.31 to -0.44, p<0.05). The study suggests that patients with chronic nonspecific neck pain who experienced significant improvements in pain intensity exhibited greater changes in cortical structure following a 10-week intervention, particularly with a combination of manual therapy and exercise. PERSPECTIVE: A combination of manual therapy and exercise results in greater improvements in clinical outcomes and substantially alters cortical thickness compared to routine physical therapy in patients with chronic nonspecific neck pain. These findings highlight the potential impact of this intervention on both brain structure and clinical recovery.

Localizing value of disturbances of self-integration, depersonalization, and forced thinking: A systematic review

We performed a systematic review of the localizational value of disturbances of self-integration, depersonalization and forced thinking in focal epilepsy with the aim to summarize the state-of-the-art anatomo-clinical correlations in the field and help guide interpretation of ictal semiology within the framework of pre-surgical evaluation. The review was performed using a PRISMA- and QUADAS2-based approach. Three separate PubMed and EMBASE searches were undertaken using the keywords self-integration, depersonalization and forced thinking, along with synonyms, in combination with terms to identify epileptogenic zone as defined by surgical outcome, MRI-findings or intracranially recorded EEG. Studies published in peer-reviewed journals with an abstract available, limited to English, French, German, Spanish, or Italian were included for review. Abstracts from scientific meetings were included if precise data on semiology in addition to either localization or surgical outcome was presented. Cases were regarded as eligible if data informing on anatomo-clinical correlations were sufficient to allow determination of an epileptogenic zone and evaluate its level of confidence. For disturbances of self-integration, the search identified 18 publications containing 23 eligible cases, with 10 additional cases identified in the literature. For depersonalization, a single case from a two patient study fulfilled inclusion criteria. For forced thinking, the search identified two publications containing four eligible cases, with six additional cases identified through literature searches. The retrieved cases suggest that disturbances of self-integration often reflect an epileptogenic zone centered around the temporoparietal region, where neighboring areas in the parietal lobe, the posterior insula, and likely depending on the type of disturbance also the adjoining occipital lobe, the anterior and middle cingulum, premotor and supplementary motor in addition to medial temporal structures could be involved. When present, lateralized symptomatology reflects a contralateral focus. Depersonalization, as a localizing ictal phenomenon was quite elusive. Forced thinking either pointed to premotor frontal or temporal epileptogenic zones. Currently, outlined epileptogenic zones of ictal disturbances of self-integration and forced thinking are quite widespread and should be regarded with a low-to-moderate degree of reliability. A focus on such rarer ictal phenomena, in combination with improved imaging techniques and increased use of SEEG, will hopefully lead to an accumulation of cases with better defined epileptogenic zones.