SPM: a history

BSA-Seg: A Bi-level sparse attention network combining narrow band loss for multi-target medical image segmentation

Segmentation of multiple targets of varying sizes within medical images is of significant importance for the diagnosis of disease and pathological research. Transformer-based methods are emerging in the medical image segmentation, leveraging the powerful yet computationally intensive self-attention mechanism. A variety of attention mechanisms have been proposed to reduce computation at the cost of accuracy loss, utilizing handcrafted patterns within local or artificially defined receptive fields. Furthermore, the common region-based loss functions are insufficient for guiding the transformer to focus on tissue regions, resulting in their unsuitability for the segmentation of tissues with intricate boundaries. This paper presents the development of a bi-level sparse attention network and a narrow band (NB) loss function for the accurate and efficient multi-target segmentation of medical images. In particular, we introduce a bi-level sparse attention module (BSAM) and formulate a segmentation network based on this module. The BSAM consists of coarse-grained patch-level attention and fine-grained pixel-level attention, which captures fine-grained contextual features in adaptive receptive fields learned by patch-level attention. This results in enhanced segmentation accuracy while simultaneously reducing computational complexity. The proposed narrow-band (NB) loss function constructs a target region in close proximity to the tissue boundary. The network is thus guided to perform boundary-aware segmentation, thereby simultaneously alleviating the issues of over-segmentation and under-segmentation. A series of comprehensive experiments on whole brains, brain tumors and abdominal organs, demonstrate that our method outperforms other state-of-the-art segmentation methods. Furthermore, the BSAM and NB loss can be applied flexibly to a variety of network frameworks.

MPRAGElike: A novel approach to generate T1w images from multi-contrast gradient echo images for brain segmentation

PURPOSE

Brain segmentation and multi-parameter mapping (MPM) are important steps in neurodegenerative disease characterization. However, acquiring both a high-resolution T1w sequence like MPRAGE (standard input to brain segmentation) and an MPM in the same neuroimaging protocol increases scan time and patient discomfort, making it difficult to combine both in clinical examinations.

METHODS

A novel approach to synthesize T1w images from MPM images, named MPRAGElike, is proposed and compared to the standard technique used to produce synthetic MPRAGE images (synMPRAGE). Twenty-three healthy subjects were scanned with the same imaging protocol at three different 7T sites using universal parallel transmit RF pulses. SNR, CNR, and automatic brain segmentation results from both MPRAGElike and synMPRAGE were compared against an acquired MPRAGE.

RESULTS

The proposed MPRAGElike technique produced higher SNR values than synMPRAGE for all regions evaluated while also having higher CNR values for subcortical structures. MPRAGE was still the image with the highest SNR values overall. For automatic brain segmentation, MPRAGElike outperformed synMPRAGE when compared to MPRAGE (median Dice Similarity Coefficient of 0.90 versus 0.29 and Average Asymmetric Surface Distance of 0.33 versus 2.93 mm, respectively), in addition to being simple, flexible, and considerably more robust to low image quality than synMPRAGE.

CONCLUSION

The MPRAGElike technique can provide a better and more reliable alternative to synMPRAGE as a substitute for MPRAGE, especially when automatic brain segmentation is of interest and scan time is limited.

Advancing Intracranial Aneurysm Detection: A Comprehensive Systematic Review and Meta-analysis of Deep Learning Models Performance, Clinical Integration, and Future Directions

BACKGROUND

Cerebral aneurysms pose a significant risk to patient safety, particularly when ruptured, emphasizing the need for early detection and accurate prediction. Traditional diagnostic methods, reliant on clinician-based evaluations, face challenges in sensitivity and consistency, prompting the exploration of deep learning (DL) systems for improved performance.

METHODS

This systematic review and meta-analysis assessed the performance of DL models in detecting and predicting intracranial aneurysms compared to clinician-based evaluations. Imaging modalities included CT angiography (CTA), digital subtraction angiography (DSA), and time-of-flight MR angiography (TOF-MRA). Data on lesion-wise sensitivity, specificity, and the impact of DL assistance on clinician performance were analyzed. Subgroup analyses evaluated DL sensitivity by aneurysm size and location, and interrater agreement was measured using Fleiss' κ.

RESULTS

DL systems achieved an overall lesion-wise sensitivity of 90 % and specificity of 94 %, outperforming human diagnostics. Clinician specificity improved significantly with DL assistance, increasing from 83 % to 85 % in the patient-wise scenario and from 93 % to 95 % in the lesion-wise scenario. Similarly, clinician sensitivity also showed notable improvement with DL assistance, rising from 82 % to 96 % in the patient-wise scenario and from 82 % to 88 % in the lesion-wise scenario. Subgroup analysis showed DL sensitivity varied with aneurysm size and location, reaching 100 % for aneurysms larger than 10 mm. Additionally, DL assistance improved interrater agreement among clinicians, with Fleiss' κ increasing from 0.668 to 0.862.

CONCLUSIONS

DL models demonstrate transformative potential in managing cerebral aneurysms by enhancing diagnostic accuracy, reducing missed cases, and supporting clinical decision-making. However, further validation in diverse clinical settings and seamless integration into standard workflows are necessary to fully realize the benefits of DL-driven diagnostics.

Serum Carotenoid Concentrations Are Associated with Enlarged Choroid Plexus, Lateral Ventricular Volume, and Perivascular Spaces on Magnetic Resonance Imaging: A Large Cohort Study

RATIONALE AND OBJECTIVES

Since carotenoids have various physiological activities, including antioxidant activity, several epidemiological studies have linked the consumption of a carotenoid-rich diet to a decreased risk of neurodegenerative diseases. Increased choroid plexus volume (CPV) and enlarged perivascular spaces (PVS) on brain magnetic resonance imaging (MRI) may be indicators of impaired glymphatic system function. The purpose of this large-scale elderly population study was to assess whether serum concentrations of major carotenoids (α-carotene, β-carotene, cis-lycopene, trans-lycopene, β-cryptoxanthin, zeaxanthin, and lutein) concentrations are associated with CPV, lateral ventricular volume (LVV), and PVS.

MATERIALS AND METHODS

This cross-sectional study included 2050 individuals (median age, 69 years; 61.02% females) who underwent 3 T MRI. The imaging characteristics included total intracranial volume (ICV), CPV, LVV, and basal ganglia-enlarged PVS on T2-weighted images.

RESULTS

Low serum β-carotene concentration was a significant independent predictor of increased CPV/ICV (p=0.046), increased LVV/ICV (p=0.035), and enlarged PVS (p=0.009) after adjusting for potential confounders (age, sex, body mass index, HbA1c level, systolic blood pressure, smoking history, drinking history, educational history, and Mini-Mental State Examination score, CRP level). Low serum α-carotene concentration was also a significant independent predictor of an enlarged PVS (p=0.014).

CONCLUSION

In this study, β-carotene concentration was associated to the CPV, LVV, and PVS, suggesting that the antioxidant activity of β-carotene may have an important role in maintaining glymphatic system function. Since β-carotene is a dietary carotenoid, our results emphasize the importance of interventions for effective β-carotene intake among elderly people.

Insight predicts subsequent memory via cortical representational change and hippocampal activity

The neural mechanisms driving creative problem-solving, including representational change and its relation to memory, still remain largely unknown. We focus on the creative process of insight, wherein rapid knowledge reorganization and integration-termed representational change-yield solutions that evoke suddenness, certainty, positive emotion, and enduring memory. We posit that this process is associated with stronger shifts in activation patterns within brain regions housing solution-relevant information, including the visual cortex for visual problems, alongside regions linked to feelings of emotion, suddenness and subsequent memory. To test this, we collect participants' brain activity while they solve visual insight problems in the MRI. Our findings substantiate these hypotheses, revealing stronger representational changes in visual cortex, coupled with activations in the amygdala and hippocampus-forming an interconnected network. Importantly, representational change and hippocampal effects are positively associated with subsequent memory. This study provides evidence of an integrated insight mechanism influencing memory.

Fronto-occipital dyscommunication associates with brain hierarchy in schizophrenia

Schizophrenia involves abnormal fronto-occipital interactions linked to hallucinations and cognitive impairments, but the neural mechanisms remain unclear. This work aims to provide an overview of the relationship between fronto-occipital dysfunction and symptoms using simultaneous EEG-fMRI data in schizophrenia. We measured the brain's functional separation and quantified bidirectional information transfer changes between the frontal and occipital regions. A pronounced elevation in correlation within the frontal lobe, accompanied by a marked reduction in the occipital lobe, was observed between gradient eccentricities and theta-power of forward waves. Moreover, the relationship between forward waves and gradient eccentricities in the ventrolateral prefrontal cortex may be shaped by positive symptoms, while the influence of negative symptoms appears to modulate the relationship between backward waves and gradient eccentricities in the insula. The MOR and CB1 neurotransmitters predominantly contributed to associations between eccentricities and traveling waves. Symptoms promote the dysregulation of hierarchical separation and information transmission in schizophrenia.

Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network

For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to effectively identify the most significant biomarkers associated with disease, we designed a novel ROI pooling score function. Additionally, we also designed a comprehensive loss function, including a group-level consistency loss function for preserving the same brain regions in subjects of the same category, and an anti-consistency function for maximizing brain region preservation differences between subjects of different categories. On the basis of the ROI graph, we directly incorporate the non-imaging information of the subjects in the network training. Experimental results on two public datasets, ABIDE and ADNI, validate the superiority of the model proposed in this paper, and the qualitative results of the biomarkers demonstrate the potential application of the model in medical diagnosis and treatment of neuropsychiatric disorders.

Pathological features of post-stroke pain: a comprehensive analysis for subtypes

Post-stroke pain is heterogeneous and includes both nociceptive and neuropathic pain. These subtypes can be comprehensively assessed using several clinical tools, such as pain-related questionnaires, quantitative somatosensory tests and brain imaging. In the present study, we conducted a comprehensive assessment of patients with central post-stroke pain and non-central post-stroke pain and analysed their clinical features. We also performed a detailed analysis of the relationships between brain lesion areas or structural disconnection of the white matter and somatosensory dysfunctions. In this multicentre cross-sectional study, 70 patients were divided into 24 with central post-stroke pain, 26 with non-central post-stroke pain and 20 with no-pain groups. Multiple logistic regression analysis was used to summarize the relationships between each pathological feature (for the central post-stroke pain and non-central post-stroke pain groups) and pain-related factors or the results of quantitative somatosensory tests. Relationships between somatosensory dysfunctions and brain lesion areas were analysed using voxel-based lesion-symptom mapping and voxel-based disconnection-symptom mapping. All pathology feature models indicated that central post-stroke pain was associated with cold hypoesthesia at 8°C (β = 2.98, odds ratio = 19.6, 95% confidence interval = 2.7-141.8), cold hyperalgesia at 8°C (β = 2.61, odds ratio = 13.6, 95% confidence interval = 1.13-163.12) and higher Neuropathic Pain Symptom Inventory scores (for spontaneous and evoked pain items only; β = 0.17, odds ratio = 1.19, 95%, confidence interval = 1.07-1.32), whereas non-central post-stroke pain was associated with joint pain (β = 5.01, odds ratio = 149.854, 95%, confidence interval = 19.93-1126.52) and lower Neuropathic Pain Symptom Inventory scores (β = -0.17, odds ratio = 0.8, 95%, confidence interval = 0.75-0.94). In the voxel-based lesion-symptom mapping, the extracted lesion areas indicated mainly voxels significantly associated with cold hyperalgesia, allodynia at 8°C and 22°C and heat hypoesthesia at 45°C. These extracted areas were mainly in the putamen, insular cortex, hippocampus, Rolandic operculum, retrolenticular part of internal and external capsules and sagittal stratum. In voxel-based disconnection-symptom mapping, the extracted disconnection maps were significantly associated with cold hyperalgesia at 8°C, and heat hypoesthesia at 37°C and 45°C. These structural disconnection patterns were mainly in the cingulum frontal parahippocampal tract, the reticulospinal tract and the superior longitudinal fasciculus with a widespread interhemispheric disconnection of the corpus callosum. These findings serve as important indicators to facilitate decision-making and optimize precision treatments through data dimensionality reduction when diagnosing post-stroke pain using clinical assessments, such as bedside quantitative sensory testing, pain-related factors, pain questionnaires and brain imaging.

Abnormal resting-state functional connectivity in adolescent depressive episodes

BACKGROUND

Depression is linked to abnormalities in brain networks. Resting-state functional connectivity (FC), as measured using resting-state fMRI (rs-fMRI), is a crucial tool for exploring the brain network abnormalities associated with depressive symptoms, as it reveals how disruptions in brain region interactions occur. However, research focusing on adolescents with depression is limited and inconsistent, highlighting the need for further studies in this area.

METHODS

Fifty-five adolescents with Depressive episodes (DE) and 26 healthy controls (HCs) underwent resting-state fMRI. Depressive symptoms were assessed using the 17-item Hamilton Rating Scale for Depression (HAMD-17). Seed regions were defined based on Yeo's seven-network scheme, including the sensorimotor network (SMN), ventral attention network (VAN), dorsal attention network (DAN), visual network (VN), frontoparietal network (FPN), default mode network (DMN), and limbic network (LN). These seed regions were derived from analysis of large-scale FC in healthy individuals, and were selected for their relevance to cognition, emotion, and depression research. Network-based statistical analyses were used to compare the adolescents with DE to the HCs, and correlation analyses were employed to examine the relationships between FC changes and cognitive performance.

RESULTS

The results showed significant differences in FC between the DE and HCs groups, involving 17 nodes and 17 edges across seven networks. Decreased FC was observed within the FPN, as well as between the FPN and VAN, the FPN and DMN, and the SMN and both the DAN and VN. Increased FC was observed between the FPN and VN, between the DAN and other networks (i.e., the DMN and FPN), and between the SMN and multiple networks. Notably, FC between the right superior parietal (SMN) and right precuneus (DMN) showed a negative correlation with HAMD-17 scores.

CONCLUSION

These results suggest that adolescents with DE experience widespread brain network abnormalities characterized by hypoactivity in external networks such as the SMN and VN, as well as hyperactivity in associative regions, including the DMN, FPN, SMN, and LN. Although these changes in FC are evident, the specific mechanisms linking them to clinical symptoms remain unclear and warrant further investigation.

Symptomatic Emotional Responses and Changes in Networks Elicited by Direct Electrical Stimulation

AIM

Emotion is a major area of research in psychology and neuroscience. However, the role of direct electrical stimulation (DES) in emotional localization has not yet been fully explored. This study aimed to analyze the use of DES in examining the local connectivity of brain regions eliciting emotional responses, thereby providing evidence for a new perspective of local changes in brain networks during emotional responses.

METHODS

We reviewed the clinical data of 500 patients with refractory epilepsy who underwent stereoencephalogram (SEEG) implantation to locate the epileptogenic area and functional mapping of the brain. The three-dimensional reconstruction was employed for the qualitative and positioning analysis on the emotional responses elicited using DES. We used Granger causality (GC), directed transfer function (DTF), and partial directed coherence (PDC) to analyze the changes in functional connectivity before and after stimulation in selected patients.

RESULTS

Emotional responses were evoked without aura using DES in 85 contacts in 31 patients, including 35 (41.2%) contacts with fear, 37 (43.5%) contacts with happiness, 6 (7.1%) contacts with anxiety, and 7 (8.2%) contacts with depression. Three contacts of interest in two patients experiencing transient emotional symptoms underwent GC, DTF, and PDC analyses; the analysis revealed significant differences in brain networks before and after stimulation in selected patients.

CONCLUSIONS

DES can evoke emotions across various brain regions, such as the bilateral amygdala, hippocampus, temporal lobe, frontal lobe, insula, cingulate cortex, paracentral gyrus, fusiform, orbitofrontal cortex, left thalamus, and putamen. These elicited emotional experiences may largely result from the alterations in local brain networks.

Relationship between White Matter Hyperintensity Volume Analyzed from Fluid-Attenuated Inversion Recovery Using a Fully Automated Analysis Software and Cognitive Impairment

INTRODUCTION

White matter hyperintensity (WMH) is associated with cognitive impairment, although the clinical significance of WMH remains unclear. We aimed to elucidate the clinical significance of WMH volume and whether a fully automated quantitative analysis of WMH would be an effective marker of cognitive function.

METHODS

Patients with suspected cognitive impairment were retrospectively examined. Clinical data, including patient information, neuropsychological examinations, diagnoses of dementia disorders, and fluid-attenuated inversion recovery (FLAIR) images, were collected. Patient information included sex, age, and educational level. Neuropsychological examinations included the Mini-Mental State Examination (MMSE) and Japanese version of the Montreal Cognitive Assessment (MoCA-J). WMH volumes were analyzed from FLAIR images using a fully automatic analysis software. The relationship between WMH volume and clinical data was investigated.

RESULTS

WMH volume was analyzed using 889 FLAIR cases. The WMH volume did not differ significantly between the sexes. WMH volume showed a positive correlation with age. Multiple comparison tests showed no significant difference in WMH volume between junior high school and high school graduates, but all other differences were significant. Multiple comparison tests revealed significant differences in mean WMH volume among all groups in the classified MMSE. The Mann-Whitney U test revealed significant differences in WMH volume between the two groups. Multiple comparison tests revealed significant differences in WMH volume among all the groups of classified diagnostic results.

CONCLUSION

Quantitative analysis of WMH volume from FLAIR images may provide useful information for dementia treatment and may be effective as a new marker in cognitive function examinations.

Multifractal dynamic changes of spontaneous brain activity in psychiatric disorders: Adult attention deficit-hyperactivity disorder, bipolar disorder, and schizophrenia

It is one of the strategies to study the complexity of spontaneous fluctuation of brain neurons based on resting-state functional magnetic resonance imaging (rs-fMRI), but the multifractal characteristics of spontaneous fluctuation of brain neurons in psychiatric diseases need to be studied. Therefore, this paper will study the multifractal spontaneous brain activity changes in psychiatric disorders using the multifractal detrended fluctuation analysis algorithm based on the UCLA datasets. Specifically: (1) multifractal characteristics in adult attention deficit-hyperactivity disorder (ADHD), bipolar disorder (BP), and schizophrenia (SCHZ); (2) the source of those multifractal characteristics. Results showed that for adult ADHD, BP, and SCHZ, all 6 functional brain regions exhibit multifractal characteristics, and the multifractal spectrum shows a reduction in bell-shaped asymmetry, unlike the intensity of healthy control (HC) asymmetry. Besides, compared with HC, the multifractal sources of all functional brain regions were fat-tail probability distribution and the long-range dependence correlation, but the intensity of fat-tail probability distribution was decreased and the long-range dependence correlation was increased. The results provide a reference for further understanding the complexity of spontaneous fluctuation of neurons in psychiatric disorders.

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data

PURPOSE

Registration of functional and structural data poses a challenge for high-resolution fMRI studies at 7 T. This study aims to develop a rapid acquisition method that provides distortion-matched, artifact-mitigated structural reference data.

METHODS

We introduce an efficient sequence protocol termed T1234, which offers adjustable distortions. This includes data that match distortions of functional data and data that are free of distortions. This approach involves a T1-weighted 2-inversion 3D-EPI sequence with four combinations of read and phase encoding directions optimized for high-resolution fMRI. A forward Bloch model was used for T1 quantification and protocol optimization. Fifteen participants were scanned at 7 T using both structural and functional protocols to evaluate the use of T1234.

RESULTS

Results from two protocols are presented. A fast distortion-free protocol reliably produced whole-brain segmentations at 0.8 mm isotropic resolution within 3:00-3:40 min. It demonstrates robustness across sessions, participants, and three different 7 T SIEMENS scanners. For a protocol with geometric distortions that matched functional data, T1234 facilitates layer-specific fMRI signal analysis with enhanced laminar precision.

CONCLUSION

This structural mapping approach enables precise registration with fMRI data. T1234 has been successfully implemented, validated, and tested, and is now available to users at our center and at over 50 centers worldwide.

Hypometabolism and atrophy patterns associated with Niemann-Pick type C

BACKGROUND

Niemann-Pick disease type C (NP-C) is a rare genetic lysosomal lipid storage disorder characterized by progressive neurological impairment. Early diagnosis is critical for initiating treatment with miglustat, which can decelerate disease progression. In this study, we evaluated a cohort of 22 NP-C patients who underwent MRI, [18F]FDG PET, and clinical assessment at baseline. We performed a cross-sectional and longitudinal imaging study evaluating the role of [18F]FDG PET as an adjunct diagnostic tool for NP-C alongside MRI, the current neuroimaging standard.

RESULTS

Group-level MRI analysis identified significant cerebellar and thalamic atrophy (d = 1.56, p < 0.0001 and d = 1.09, p < 0.001, respectively), with less pronounced involvement of the frontal lobe and hippocampus, which aligned with existing neuropathological understanding and guidelines. Conversely, [18F]FDG PET imaging revealed extensive hypometabolism in the cerebellum, thalamus, and cingulate cortex (d = 1.42, p < 0.0001), and moderate hypometabolism in broad frontotemporal areas. [18F]FDG PET provided higher effect sizes across all brain regions, including regions without apparent atrophy, which suggests that it may be more sensitive than MRI for detecting NP-C neurodegenerative changes. Single-subject visual assessment of individual PET images further validated the clinical utility of [18F]FDG PET, with significant hypometabolism observed in the cerebellum, thalamus and anterior and posterior cingulate reported by physicians in 17/22 patients. Both hypometabolism and atrophy in the cerebellum were associated with ataxia, (more strongly indicated by [18F]FDG PET, p < 0.0001 vs. MRI, p = 0.07). Medial temporal lobe atrophy was associated with cognitive impairment (p < 0.05), and frontal hypometabolism was slightly related to behavioural impairment (p < 0.07). Longitudinal [18F]FDG PET analysis revealed progressive subcortical, cortical and cerebellar hypometabolism, which was most pronounced in the cerebellum (-12% per year, p < 0.001). Patients treated with miglustat showed a trend towards attenuated cerebellar hypometabolism progression compared to untreated patients (p = 0.10).

CONCLUSIONS

Our findings delineate a discernible hypometabolism pattern specific to NP-C that distinguishes it from other neurodegenerative conditions, thus suggesting that [18F]FDG PET might be a promising tool for NP-C diagnosis and to study disease progression.

TRIAL REGISTRATION

XUNTA 2015/140. Registered 21 April 2015.

Field-cycling imaging yields repeatable brain R1 dispersion measurement at fields strengths below 0.2 Tesla with optimal fitting routine

OBJECTIVES

By rapidly changing magnetic field strength between 0.2 and 200 mT during the pulse sequence Field-Cycling Imaging (FCI) makes it possible to identify and evaluate new quantitative markers of pathology derived from dispersion of spin-lattice relaxation rate (R1) in vivo. The aim of this work was to determine the most effective approach to reliably estimate multi-field R1 dispersion measurements in brain tissue using FCI.

MATERIALS AND METHODS

This repeatability study consisted of twenty participants with moderate or severe small vessel disease. Each participant underwent 3 T MRI and FCI scans, repeated 30 days apart. After R1 maps were generated at 0.2, 2, 20, and 200 mT, co-registered tissue labels generated from 3 T MRI were used to extract tissue averaged values of R1 dispersion from regions of white matter (WM) and WM hyperintensities (WMHs).

RESULTS

The fitted model which yielded best overall image contrast between WM and WMH regions and R1 dispersion model adherence was determined. Tissue averaged values of R1 (0.2 mT) and R1 dispersion slope exhibited Cohen's d effect sizes of 3.07 and 1.48, respectively, between regions of WM and WMH. The cohort study results were repeatable between study visits.

DISCUSSION

Differences in R1 measurements could repeatably be discerned between normal and abnormal appearing brain tissues.

Source Imaging Method Based on Spatial Smoothing and Edge Sparsity (SISSES) and Its Application to OPM-MEG

Source estimation in magnetoencephalography (MEG) involves solving a highly ill-posed problem without a unique solution. Accurate estimation of the time course and spatial extent of the source is important for studying the mechanisms of brain activity and preoperative functional localization. Traditional methods tend to yield small-amplitude diffuse or large-amplitude focused source estimates. Recently, the structured sparsity-based source imaging algorithm has emerged as one of the most promising algorithms for improving source extent estimation. However, it suffers from a notable amplitude bias. To improve the spatiotemporal resolution of reconstructed sources, we propose a novel method called the source imaging method based on spatial smoothing and edge sparsity (SISSES). In this method, the temporal dynamics of sources are modeled using a set of temporal basis functions, and the spatial characteristics of the source are represented by a first-order Markov random field (MRF) model. In particular, sparse constraints are imposed on the MRF model residuals in the original and variation domains. Numerical simulations were conducted to validate the SISSES. The results demonstrate that SISSES outperforms benchmark methods for estimating the time course, location, and extent of patch sources. Additionally, auditory and median nerve stimulation experiments were performed using a 31-channel optically pumped magnetometer MEG system, and the SISSES was applied to the source imaging of these data. The results demonstrate that SISSES correctly identified the source regions in which brain responses occurred at different times, demonstrating its feasibility for various practical applications.

Differed brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis patients

PURPOSE

To investigate the differences in brain spontaneous neural activity between limb-onset and bulbar-onset amyotrophic lateral sclerosis (ALS-L and ALS-B, respectively) patients using resting-state functional MRI (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo).

MATERIALS AND METHODS

The rs-fMRI data were collected from 41 ALS patients (11 ALS-B and 30 ALS-L) and 25 healthy controls (HC). ALFF and ReHo values were calculated, and group differences were assessed using one-way ANCOVA and two-sample t-tests. Correlation analyses with clinical measures were conducted. Support vector machine (SVM) analysis was performed to distinguish ALS subtypes.

RESULTS

Compared with ALS-L, ALS-B showed increased ALFF values in the right gyrus rectus/ orbital part of right middle frontal gyrus, orbital part of left middle frontal gyrus and left dorsolateral superior frontal gyrus/ left medial superior frontal gyrus and decreased ALFF values in the left superior occipital gyrus (FDR-corrected, P < 0.05). Both ALS subtypes demonstrated distinct ALFF alterations compared to HC. Differences in ReHo values were only found between ALS-B and HC. Correlation analyses revealed associations between ALFF in specific brain regions and ALS clinical scores. SVM analysis achieved an accuracy of 90.2 %, with an AUC of 0.909 in differentiating ALS-B and ALS-L.

CONCLUSION

ALS-B and ALS-L patients had distinct alterations in brain spontaneous neural activity, which could serve as potential biomarkers for accurately distinguishing these two subtypes. Our findings offer a new insight into the neural mechanism of ALS, underscoring the importance of personalized diagnostic approaches for this complex neurological disorder.

Dysfunctional large-scale brain networks in drug-naïve depersonalization-derealization disorder patients

BACKGROUND

Depersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.

METHODS

A total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within-and between-network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large-scale brain networks and scores of clinical assessments in DPRD patients.

RESULTS

Compared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.

CONCLUSION

Overall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.

Gray matter volume differences based on sex in first-episode drug-naive patients with major depressive disorder and its molecular analysis

This study analyzed whether gray matter volume (GMV) differences exist between the sexes in patients with major depressive disorder (MDD) and explored the relationships between these differences and neurotransmitter systems. This study enrolled 190 first-episode drug-naive patients with MDD and 293 healthy controls. All participants underwent T1-weighted high-resolution MRI. The interaction between the diagnosis (healthy controls vs. MDD) and sex (male vs. female) regarding GMV alterations was analyzed. The JuSpace toolbox, which covers a wide range of neurotransmitter systems, was used to identify the relationship between MDD-induced and sex-induced GMV alterations and specific receptor/transporter proteins in the brain. Sex-specific GMV differences were observed in the healthy controls but not in MDD patients. Male healthy controls had a larger GMV in the bilateral parahippocampal, lingual, inferior occipital, fusiform, cerebellar subregions, and left inferior temporal than female healthy controls, but several subregions of the thalamus had a larger GMV in female healthy controls than in male healthy controls. Sex-induced GMV alterations were associated with 5-hydroxytryptamine receptor subtype 1a, cannabinoid receptor, and dopamine receptor ( P  < 0.01, false discovery rate corrected). GMV differences were not detected in the main effect of diagnosis and the interaction of diagnosis and sex. Sex-specific GMV differences are associated with the spatial distribution of serotonin, dopamine, and cannabinoid neurotransmitter receptor systems. Sex-based physiological differences in the GMV may account for male and female susceptibility to and differences in the clinical symptoms of MDD.

Processing, evaluating, and understanding FMRI data with afni_proc.py

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but also first outputs a fully commented processing script that the users can read, query, interpret, and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting-state FMRI example commands.

Mesostriatal Dopaminergic Circuit Dysfunction in Schizophrenia: A Multimodal Neuromelanin-Sensitive Magnetic Resonance Imaging and [18F]-DOPA Positron Emission Tomography Study

BACKGROUND

Striatal hyperdopaminergia is implicated in the pathoetiology of schizophrenia, but how this relates to dopaminergic midbrain activity is unclear. Neuromelanin (NM)-sensitive magnetic resonance imaging provides a marker of long-term dopamine function. We examined whether midbrain NM-sensitive magnetic resonance imaging contrast-to-noise ratio (NM-CNR) was higher in people with schizophrenia than in healthy control (HC) participants and whether this correlated with dopamine synthesis capacity.

METHODS

One hundred fifty-four participants (schizophrenia group: n = 74, HC group: n = 80) underwent NM-sensitive magnetic resonance imaging of the substantia nigra and ventral tegmental area (SN-VTA). A subset of the schizophrenia group (n = 38) also received [18F]-DOPA positron emission tomography to measure dopamine synthesis capacity (Kicer) in the SN-VTA and striatum.

RESULTS

SN-VTA NM-CNR was significantly higher in patients with schizophrenia than in HC participants (effect size = 0.38, p = .019). This effect was greatest for voxels in the medial and ventral SN-VTA. In patients, SN-VTA Kicer positively correlated with SN-VTA NM-CNR (r = 0.44, p = .005) and striatal Kicer (r = 0.71, p < .001). Voxelwise analysis demonstrated that SN-VTA NM-CNR was positively associated with striatal Kicer (r = 0.53, p = .005) and that this relationship seemed strongest between the ventral SN-VTA and associative striatum in schizophrenia.

CONCLUSIONS

Our results suggest that NM levels are higher in patients with schizophrenia than in HC individuals, particularly in midbrain regions that project to parts of the striatum that receive innervation from the limbic and association cortices. The direct relationship between measures of NM and dopamine synthesis suggests that these aspects of schizophrenia pathophysiology are linked. Our findings highlight specific mesostriatal circuits as the loci of dopamine dysfunction in schizophrenia and thus as potential therapeutic targets.

Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis

BACKGROUND

Depersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.

METHODS

In our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on brain dynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.

RESULTS

We identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.

CONCLUSIONS

Our study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients.

TRIAL REGISTRATIONS

The trial was registered at the Chinese Clinical Trial Registry on 03/01/2021 (Registration number: ChiCTR2100041741, https://www.chictr.org.cn/showproj.html?proj=66731 ) before the trial.

Comparison of autism spectrum disorder subtypes based on functional and structural factors

Autism spectrum disorder (ASD) is a series of neurodevelopmental disorders that may affect a patient's social, behavioral, and communication abilities. As a typical mental illness, ASD is not a single disorder. ASD is often divided into subtypes, such as autism, Asperger's, and pervasive developmental disorder-not otherwise specified (PDD-NOS). Studying the differences among brain networks of the subtypes has great significance for the diagnosis and treatment of ASD. To date, many studies have analyzed the brain activity of ASD as a single mental disorder, whereas few have focused on its subtypes. To address this problem, we explored whether indices derived from functional and structural magnetic resonance imaging (MRI) data exhibited significant dissimilarities between subtypes. Utilizing a brain pattern feature extraction method from fMRI based on tensor decomposition, amplitude of low-frequency fluctuation and its fractional values of fMRI, and gray matter volume derived from MRI, impairments of function in the subcortical network and default mode network of autism were found to lead to major differences from the other two subtypes. Our results provide a systematic comparison of the three common ASD subtypes, which may provide evidence for the discrimination between ASD subtypes.

T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data

Purpose

High-resolution fMRI at 7T is challenged by suboptimal alignment quality between functional data and structural scans. This study aims to develop a rapid acquisition method that provides distortion-matched, artifact-mitigated structural reference data.

Methods

We introduce an efficient sequence protocol termed T1234, which offers adjustable distortions. This approach involves a T1-weighted 2-inversion 3D-EPI sequence with four spatial encoding directions optimized for high-resolution fMRI. A forward Bloch model was used for T1 quantification and protocol optimization. Twenty participants were scanned at 7T using both structural and functional protocols to evaluate the utility of T1234.

Results

Results from two protocols are presented. A fast distortion-free protocol reliably produced whole-brain segmentations at 0.8mm isotropic resolution within 3:00-3:40 minutes. It demonstrates robustness across sessions, participants, and three different 7T SIEMENS scanners. For a protocol with geometric distortions that matched functional data, T1234 facilitates layer-specific fMRI signal analysis with enhanced laminar precision.

Conclusion

This structural mapping approach enables precise registration with fMRI data. T1234 has been successfully implemented, validated, and tested, and is now available to users at our center and at over 50 centers worldwide.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Mouse Brain Extractor: Brain segmentation of mouse MRI using global positional encoding and SwinUNETR

In spite of the great progress that has been made towards automating brain extraction in human magnetic resonance imaging (MRI), challenges remain in the automation of this task for mouse models of brain disorders. Researchers often resort to editing brain segmentation results manually when automated methods fail to produce accurate delineations. However, manual corrections can be labor-intensive and introduce interrater variability. This motivated our development of a new deep-learning-based method for brain segmentation of mouse MRI, which we call Mouse Brain Extractor. We adapted the existing SwinUNETR architecture (Hatamizadeh et al., 2021) with the goal of making it more robust to scale variance. Our approach is to supply the network model with supplementary spatial information in the form of absolute positional encoding. We use a new scheme for positional encoding, which we call Global Positional Encoding (GPE). GPE is based on a shared coordinate frame that is relative to the entire input image. This differs from the positional encoding used in SwinUNETR, which solely employs relative pairwise image patch positions. GPE also differs from the conventional absolute positional encoding approach, which encodes position relative to a subimage rather than the entire image. We trained and tested our method on a heterogeneous dataset of N=223 mouse MRI, for which we generated a corresponding set of manually-edited brain masks. These data were acquired previously in other studies using several different scanners and imaging protocols and included in vivo and ex vivo images of mice with heterogeneous brain structure due to different genotypes, strains, diseases, ages, and sexes. We evaluated our method's results against those of seven existing rodent brain extraction methods and two state-of-the art deep-learning approaches, nnU-Net (Isensee et al., 2018) and SwinUNETR. Overall, our proposed method achieved average Dice scores on the order of 0.98 and average HD95 measures on the order of 100 μm when compared to the manually-labeled brain masks. In statistical analyses, our method significantly outperformed the conventional approaches and performed as well as or significantly better than the nnU-Net and SwinUNETR methods. These results suggest that Global Positional Encoding provides additional contextual information that enables our Mouse Brain Extractor to perform competitively on datasets containing multiple resolutions.

[Lower limb joint contact forces and ground reaction forces analysis based on Azure Kinect motion capture]

Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.

Altered regional neural activity and functional connectivity in patients with non-communicating hydrocephalus: a resting-state functional magnetic resonance imaging study

Introduction

Cognitive impairment is a frequent clinical symptom of non-communicating hydrocephalus (NCH) involving multiple domains, including executive function, working memory, visual-spatial function, language, and attention. Functional magnetic resonance imaging (fMRI) can be used to obtain information on functional activity in local brain areas and functional connectivity (FC) across multiple brain regions. However, studies on the associated cognitive impairment are limited; further, the pathophysiological mechanisms of NCH with cognitive impairment remain unclear. Here, we aimed to explore alterations in regional neural activity and FC, as well as the mechanisms of cognitive impairment, in patients with NCH.

Methods

Overall, 16 patients with NCH and 25 demographically matched healthy controls (HCs) were assessed using the Mini-Mental State Examination (MMSE) and fMRI. Changes in regional homogeneity (ReHo), degree centrality (DC), and region of interest-based FC were analyzed in both groups. The relationship between fMRI metrics (ReHo, DC, and FC) and MMSE scores in patients with NCH was also investigated.

Results and discussion

Compared with the HC group, the NCH group exhibited significantly lower ReHo values in the left precentral and postcentral gyri, and significantly higher ReHo values in the left medial prefrontal cortex (MPFC). The NCH group also showed significantly higher DC values in the bilateral MPFC compared with the HC group. Regarding seed-based FC, the MPFC showed reduced FC values in the right superior parietal and postcentral gyrus in the NCH group compared with those in the HC group. Moreover, within the NCH group, MMSE scores were significantly negatively correlated with the ReHo value in the left MPFC and the DC value in the bilateral MPFC, whereas MMSE scores were significantly positively correlated with FC values. To conclude, regional neural activity and FC are altered in patients with NCH and are correlated with cognitive impairment. These results advance our understanding of the pathophysiological mechanisms underlying the association between NCH and cognitive impairment.

Chronic hypercortisolism disrupts the principal functional gradient in Cushing's disease: A multi-scale connectomics and transcriptomics study

Cushing's disease (CD) represents a state of cortisol excess, serving as a model to investigate the effects of prolonged hypercortisolism on functional brain. Potential alterations in the functional connectome of the brain may explain frequently reported cognitive deficits and affective disorders in CD patients. This study aims to elucidate the effects of chronic hypercortisolism on the principal functional gradient, which represents a hierarchical architecture with gradual transitions across cognitive processes, by integrating connectomics and transcriptomics approaches. Utilizing resting-state functional magnetic resonance imaging data from 140 participants (86 CD patients, 54 healthy controls) recruited at a single center, we explored the alterations in the principal gradient in CD patients. Further, we thoroughly explored the underlying associative mechanisms of the observed characteristic alterations with cognitive function domains, biological attributes, and neuropsychiatric representations, as well as gene expression profiles. Compared to healthy controls, CD patients demonstrated changes in connectome patterns in both primary and higher-order networks, exhibiting an overall converged trend along the principal gradient axis. The gradient values in CD patients' right prefrontal cortex and bilateral sensorimotor cortices exhibited a significant correlation with cortisol levels. Moreover, the cortical regions showing gradient alterations were principally associated with sensory information processing and higher-cognitive functions, as well as correlated with the gene expression patterns which involved synaptic components and function. The findings suggest that converged alterations in the principal gradient in CD patients may mediate the relationship between hypercortisolism and cognitive impairments, potentially involving genes regulating synaptic components and function.

Transcriptomic decoding of regional cortical vulnerability to major depressive disorder

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.

Choroid plexus enlargement in mild cognitive impairment on MRI: a large cohort study

OBJECTIVES

Previous studies have shown possible choroid plexus (CP) dysfunction in Alzheimer's disease (AD) and highlighted CP enlargement on magnetic resonance imaging (MRI) as a predictive factor of AD. However, few studies have assessed the relationship between CP volume (CPV) and mild cognitive impairment (MCI). In this large elderly population study, we investigated the changes in CPV in patients with MCI using MRI above 65 years.

METHODS

This cross-sectional study included 2144 participants (median age, 69 years; 60.9% females) who underwent 3T MRI; they were grouped as 218 MCI participants and 1904 cognitively healthy controls. The total intracranial volume (ICV), total brain volume (TBV), CPV, hippocampal volume (HV), and lateral ventricle volume (LVV) were calculated.

RESULTS

CPV/ICV was a significant independent predictor of MCI (p < 0.01) after adjusting for potential confounders (age, sex, hypertension, hyperlipidemia, diabetes, and education level). The CPV/ICV ratio was also a significant independent predictor of MCI after adjusting for the TBV/ICV ratio (p = 0.022) or HV/ICV ratio (p = 0.017), in addition to potential confounders. The CPV was significantly correlated with the LVV (r = 0.97, p < 0.01).

CONCLUSION

We identified a relationship between CPV and MCI, which could not be explained by the degree of brain atrophy. Our results support CP dysfunction in MCI.

CLINICAL RELEVANCE STATEMENT

Choroid plexus volume measurement may serve as a valuable imaging biomarker for diagnosing and monitoring mild cognitive impairment. The enlargement of the choroid plexus, independent of brain atrophy, suggests its potential role in mild cognitive impairment pathology.

KEY POINTS

• The study examines choroid plexus volume in relation to cognitive decline in elderly. • Enlarged choroid plexus volume independently indicates mild cognitive impairment presence. • Choroid plexus volume could be a specific biomarker for early mild cognitive impairment diagnosis.

Validation of daily 0.35 T diffusion-weighted MRI for MRI-guided glioblastoma radiotherapy

BACKGROUND

MRI-Linac systems enable daily diffusion-weighed imaging (DWI) MRI scans for assessing glioblastoma tumor changes with radiotherapy treatment.

PURPOSE

Our study assessed the image quality of echoplanar imaging (EPI)-DWI scans compared with turbo spin echo (TSE)-DWI scans at 0.35 Tesla (T) and compared the apparent diffusion coefficient (ADC) values and distortion of EPI-DWI on 0.35 T MRI-Linac compared to high-field diagnostic MRI scanners.

METHODS

The calibrated National Institute of Standards and Technology (NIST)/Quantitative Imaging Biomarkers Alliance (QIBA) Diffusion Phantom was scanned on a 0.35 T MRI-Linac, and 1.5 T and 3 T MRI with EPI-DWI. Five patients were scanned on a 0.35 T MRI-Linac with a TSE-DWI sequence, and five other patients were scanned with EPI-DWI on a 0.35 T MRI-Linac and a 3 T MRI. The quality of images was compared between the TSE-DWI and EPI-DWI on the 0.35 T MRI-Linac assessing signal-to-noise ratios and presence of artifacts. EPI-DWI ADC values and distortion magnitude were measured and compared between 0.35 T MRI-Linac and high-field MRI for both phantom and patient studies.

RESULTS

The average ADC differences between EPI-DWI acquired on the 0.35 T MRI-Linac, 1.5 T and 3 T MRI scanners and published references in the phantom study were 1.7%, 0.4% and 1.0%, respectively. Comparing the ADC values based on EPI-DWI in glioblastoma tumors, there was a 3.36% difference between 0.35 and 3 T measurements. Susceptibility-induced distortions in the EPI-DWI phantoms were 0.46 ± 1.51 mm for 0.35 MRI-Linac, 0.98 ± 0.51 mm for 1.5 T MRI and 1.14 ± 1.88 mm for 3 T MRI; for patients -0.47 ± 0.78 mm for 0.35 T and 1.73 ± 2.11 mm for 3 T MRIs. The mean deformable registration distortion for a phantom was 1.1 ± 0.22 mm, 3.5 ± 0.39 mm and 4.7 ± 0.37 mm for the 0.35 T MRI-Linac, 1.5 T MRI, and 3 T MRI scanners, respectively; for patients this distortion was -0.46 ± 0.57 mm for 0.35 T and 4.2 ± 0.41 mm for 3 T. EPI-DWI 0.35 T MRI-Linac images showed higher SNR and lack of artifacts compared with TSE-DWI, especially at higher b-values up to 1000 s/mm2.

CONCLUSION

EPI-DWI on a 0.35 T MRI-Linac showed superior image quality compared with TSE-DWI, minor and less distortions than high-field diagnostic scanners, and comparable ADC values in phantoms and glioblastoma tumors. EPI-DWI should be investigated on the 0.35 T MRI-Linac for prediction of early response in patients with glioblastoma.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block," as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation

BACKGROUND

Transcranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed.

NEW METHOD

The pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers.

RESULTS

Models of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ.

COMPARISON WITH EXISTING METHODS

Current methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers.

CONCLUSION

The pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

FLAIR Hyperintensities in the Anterior Part of the Callosal Splenium in the Elderly Population: A Large Cohort Study

RATIONALE AND OBJECTIVES

Although hyperintensity in the anterior portion of the callosal splenium on FLAIR (aCS-hyperintensity) is a common finding in elderly adults, no previous studies have examined the clinical significance. In this large elderly population study, we aimed to investigate the associations of aCS-hyperintensity with vascular risk factors, cognitive decline, and other MRI measurements.

MATERIALS AND METHODS

This cross-sectional study included 2110 participants (median age, 69 years; 61.1% females) who underwent 3 T MRI. The participants were grouped as 215 with mild cognitive impairment (MCI) and 1895 cognitively normal older adults (NOAs). Two neuroradiologists evaluated aCS-hyperintensity by using a four-point scale (none, mild, moderate, and severe). Periventricular hyperintensities (PVHs) were also rated on a four-point scale according to the Fazekas scale. The total intracranial volume (ICV), total brain volume, choroid plexus volume (CPV), and lateral ventricle volume (LVV) were calculated.

RESULTS

Logistic regression analysis showed diabetes was the main predictor of aCS-hyperintensity after adjusting for potential confounders (age, sex, hypertension, and hyperlipidemia) (p < 0.01), whereas PVH was associated with hypertension (p < 0.01). aCS-hyperintensity rated as "severe" was associated with a presence of MCI (p < 0.01). For the imaging factors, LVV was an independent predictor of aCS-hyperintensity when brain volume and PVH grade were added to the analysis (p < 0.01).

CONCLUSION

Cerebral small vessel disease due to diabetes is a major contributor to the development of aCS-hyperintensity. Cerebrospinal fluid clearance failure may also relate to aCS-hyperintensity, which may offer new insights into the pathologic processes underlying MCI.

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox

The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Comparison of arterial spin labeled MRI (ASL MRI) between ADHD and control group (ages of 6-12)

This study utilized arterial spin labeling-magnetic resonance imaging (ASL-MRI) to explore the developmental trajectory of brain activity associated with attention deficit hyperactivity disorder (ADHD). Pulsed arterial spin labeling (ASL) data were acquired from 157 children with ADHD and 109 children in a control group, all aged 6-12 years old. Participants were categorized into the age groups of 6-7, 8-9, and 10-12, after which comparisons were performed between each age group for ASL analysis of cerebral blood flow (CBF). In total, the ADHD group exhibited significantly lower CBF in the left superior temporal gyrus and right middle frontal gyrus regions than the control group. Further analysis revealed: (1) The comparison between the ADHD group (N = 70) aged 6-7 and the age-matched control group (N = 33) showed no statistically significant difference between. (2) However, compared with the control group aged 8-9 (N = 39), the ADHD group of the same age (N = 53) showed significantly lower CBF in the left postcentral gyrus and left middle frontal gyrus regions. (3) Further, the ADHD group aged 10-12 (N = 34) demonstrated significantly lower CBF in the left superior occipital region than the age-matched control group (N = 37). These age-specific differences suggest variations in ADHD-related domains during brain development post age 6-7.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically under-discussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include: a modular HTML document that covers full single subject processing from the raw data through statistical modeling; several review scripts in the results directory of processed data; and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block", as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

Stable multivariate lesion symptom mapping

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

Kinetic and kinematic profile of eccentric quasi-isometric loading

Eccentric quasi-isometric (EQI) contractions (maintaining a yielding contraction for as long as possible, beyond task failure) have gained interest in research and applied settings. However, little is known regarding the biomechanical profile of EQIs. Fourteen well-trained males performed four maximal effort knee-extensor EQIs, separated by 180 seconds. Angular impulse, velocity, and time-under-tension through the 30-100º range of motion (ROM), and in eight ROM brackets were quantified. Statistical parametric mapping, analyses of variance, and standardised effects (Hedges' g (ES), %Δ) detected between-contraction joint-angle-specific differences in time-normalised and absolute variables. Mean velocity was 1.34º·s-1 with most (62.5 ± 4.9%) of the angular impulse imparted between 40-70º. Most between-contraction changes occurred between 30-50º (p≤ 0.067, ES = 0.53 ± 0.31, 60 ± 52%), while measures remained constant between 50-100º (= 0.069-0.83, ES = 0.10 ± 0.26, 14.3 ± 24.6%). EQIs are a time-efficient means to impart high cumulative mechanical tension, especially at short to medium muscle lengths. However, angular impulse distribution shifts towards medium to long muscle lengths with repeat contractions. Practitioners may utilise EQIs to emphasize the initial portion of the ROM, and limit ROM, or apply EQIs in a fatigued state to emphasize longer muscle lengths.

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

Comparison of qualitative and fully automated quantitative tools for classifying severity of white matter hyperintensity

OBJECTIVE

In this study, we aimed to compare the Fazekas scoring system and quantitative white matter hyperintensity volume in the classification of white matter hyperintensity severity using a fully automated analysis software to investigate the reliability of quantitative evaluation.

MATERIALS AND METHODS

Patients with suspected cognitive impairment who underwent medical examinations at our institution between January 2010 and May 2021 were retrospectively examined. White matter hyperintensity volumes were analyzed using fully automated analysis software and Fazekas scoring (scores 0-3). Using one-way analysis of variance, white matter hyperintensity volume differences across Fazekas scores were assessed. We employed post-hoc pairwise comparisons to compare the differences in the mean white matter hyperintensity volume between each Fazekas score. Spearman's rank correlation test was used to investigate the association between Fazekas score and white matter hyperintensity volume.

RESULTS

Among the 839 patients included in this study, Fazekas scores 0, 1, 2, and 3 were assigned to 68, 198, 217, and 356 patients, respectively. White matter hyperintensity volumes significantly differed according to Fazekas score (F=623.5, p<0.001). Post-hoc pairwise comparisons revealed significant differences in mean white matter hyperintensity volume between all Fazekas scores (p<0.05). We observed a significantly positive correlation between the Fazekas scores and white matter hyperintensity volume (R=0.823, p<0.01).

CONCLUSIONS

Quantitative white matter hyperintensity volume and the Fazekas scores are highly correlated and may be used as indicators of white matter hyperintensity severity. In addition, quantitative analysis may be more effective in classifying advanced white matter hyperintensity lesions than the Fazekas classification.

Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network

BACKGROUND

Whole brain delineation (WBD) is utilized in neuroimaging analysis for data preprocessing and deriving whole brain image metrics. Current automated WBD techniques for analysis of preclinical brain MRI data show limited accuracy when images present with significant neuropathology and anatomical deformations, such as that resulting from organophosphate intoxication (OPI) and Alzheimer's Disease (AD), and inadequate generalizability.

METHODS

A modified 2D U-Net framework was employed for WBD of MRI rodent brains, consisting of 27 convolutional layers, batch normalization, two dropout layers and data augmentation, after training parameter optimization. A total of 265 T2-weighted 7.0 T MRI scans were utilized for the study, including 125 scans of an OPI rat model for neural network training. For testing and validation, 20 OPI rat scans and 120 scans of an AD rat model were utilized. U-Net performance was evaluated using Dice coefficients (DC) and Hausdorff distances (HD) between the U-Net-generated and manually segmented WBDs.

RESULTS

The U-Net achieved a DC (median[range]) of 0.984[0.936-0.990] and HD of 1.69[1.01-6.78] mm for OPI rat model scans, and a DC (mean[range]) of 0.975[0.898-0.991] and HD of 1.49[0.86-3.89] for the AD rat model scans.

COMPARISON WITH EXISTING METHODS

The proposed approach is fully automated and robust across two rat strains and longitudinal brain changes with a computational speed of 8 seconds/scan, overcoming limitations of manual segmentation.

CONCLUSIONS

The modified 2D U-Net provided a fully automated, efficient, and generalizable segmentation approach that achieved high accuracy across two disparate rat models of neurological diseases.

Safety and Efficacy of Deep Transcranial Magnetic Stimulation for Management of Emotional Dysregulation in Children and Adolescents with Externalizing Behavior Disorders: Protocol of a Transdiagnostic Sham Controlled fMRI Study

Background

High-frequency deep transcranial magnetic stimulation (dTMS) on the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC) has been known to be effective in modulating emotional experience but not studied in children and adolescents with externalizing behavior disorders (EBDs). We present a novel protocol for a study that aims to assess the safety and efficacy of adjuvant dTMS in managing emotional dysregulation in EBDs in children and adolescents.

Methods

The trial is prospectively registered in the Clinical Trial Registry of India (CTRI) at www.ctri.nic.in with registration number: CTRI/2023/03/050701. In total, 40 subjects with age less than 18 years with EBDs would be randomized into two groups (active and sham dTMS); receiving 15 sessions of high-frequency dTMS, each, over 3 weeks. The subjects and rater would remain blind to treatment allocation. Assessments would be done at baseline and immediately after completion of the treatment using the Child Behavior Checklist (CBCL), Difficulty in Emotional Regulation Scale (DERS), Modified Overt Aggression Scale (MOAS), Affective Reactivity Index (ARI), Barratt's Impulsivity Scale (BIS), Drug Abuse Screening Test (DAST), Children Global Assessment Scale (CGAS), and Clinical Global Impression (CGI). A checklist for side effects will be administered following each session in both groups.

Result

Data shall be analyzed utilizing the statistical software Statistical Package for Social Sciences for outcome variables as defined for the purpose of the study. Safety of dTMS in young subjects as assessed by TMSens_Q and reduction in scores of DERS would be primary outcome variables. Functional Magnetic Resonance Imaging (fMRI) task-based assessment of the difference in activation of mPFC and ACC at baseline and after application of dTMS and reduction in scores of BIS, ARI, MOAS, CGI, and CGAS would be measured as secondary outcome variables.

Conclusion

The study's results are going to provide insight into potential role of dTMS in addressing emotional dysregulation in EBDs in children and adolescents adding one more tool to the armamentarium.

LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION

Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.