Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale.

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Characteristic BOLD signals are detectable in white matter of the spinal cord at rest and after a stimulus.

We report the reliable detection of reproducible patterns of blood-oxygenation-level-dependent (BOLD) MRI signals within the white matter (WM) of the spinal cord during a task and in a resting state. Previous functional MRI studies have shown that BOLD signals are robustly detectable not only in gray matter (GM) in the brain but also in cerebral WM as well as the GM within the spinal cord, but similar signals in WM of the spinal cord have been overlooked. In this study, we detected BOLD signals in the WM of the spinal cord in squirrel monkeys and studied their relationships with the locations and functions of ascending and descending WM tracts. Tactile sensory stimulus -evoked BOLD signal changes were detected in the ascending tracts of the spinal cord using a general-linear model. Power spectral analysis confirmed that the amplitude at the fundamental frequency of the response to a periodic stimulus was significantly higher in the ascending tracts than the descending ones. Independent component analysis of resting-state signals identified coherent fluctuations from eight WM hubs which correspond closely to the known anatomical locations of the major WM tracts. Resting-state analyses showed that the WM hubs exhibited correlated signal fluctuations across spinal cord segments in reproducible patterns that correspond well with the known neurobiological functions of WM tracts in the spinal cord. Overall, these findings provide evidence of a functional organization of intraspinal WM tracts and confirm that they produce hemodynamic responses similar to GM both at baseline and under stimulus conditions.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis.

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury.

Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel-wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4-6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.

Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy.

OBJECTIVE: In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS: TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS: A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION: A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.

Lifetime Stressful Events Associated with Alzheimer's Pathologies, Neuroinflammation and Brain Structure in a Risk Enriched Cohort.

OBJECTIVE: Along with the known effects of stress on brain structure and inflammatory processes, increasing evidence suggest a role of chronic stress in the pathogenesis of Alzheimer's disease (AD). We investigated the association of accumulated stressful life events (SLEs) with AD pathologies, neuroinflammation, and gray matter (GM) volume among cognitively unimpaired (CU) individuals at heightened risk of AD. METHODS: This cross-sectional cohort study included 1,290 CU participants (aged 48-77) from the ALFA cohort with SLE, lumbar puncture (n = 393), and/or structural magnetic resonance imaging (n = 1,234) assessments. Using multiple regression analyses, we examined the associations of total SLEs with cerebrospinal fluid (1) phosphorylated (p)-tau181 and Aß1-42/1-40 ratio, (2) interleukin 6 (IL-6), and (3) GM volumes voxel-wise. Further, we performed stratified and interaction analyses with sex, history of psychiatric disease, and evaluated SLEs during specific life periods. RESULTS: Within the whole sample, only childhood and midlife SLEs, but not total SLEs, were associated with AD pathophysiology and neuroinflammation. Among those with a history of psychiatric disease SLEs were associated with higher p-tau181 and IL-6. Participants with history of psychiatric disease and men, showed lower Aß1-42/1-40 with higher SLEs. Participants with history of psychiatric disease and women showed reduced GM volumes in somatic regions and prefrontal and limbic regions, respectively. INTERPRETATION: We did not find evidence supporting the association of total SLEs with AD, neuroinflammation, and atrophy pathways. Instead, the associations appear to be contingent on events occurring during early and midlife, sex and history of psychiatric disease. ANN NEUROL 2024;95:1058-1068.

White and Gray Matter Changes are Associated With Neurocognitive Decline in HIV Infection.

OBJECTIVE: To investigate the relationship between neurocognitive deficits and structural changes on brain magnetic resonance imaging in people living with HIV (PLWH) with good virological control on combination antiretroviral therapy, compared with socioeconomically matched control participants recruited from the same communities. METHODS: Brain magnetic resonance imaging scans, and clinical and neuropsychological data were obtained from virologically controlled PLWH (viral load of <50 c/mL and at least 1 year of combination antiretroviral therapy) and socioeconomically matched control participants. Magnetic resonance imaging was carried out on 3 T scanner with 8-channel head coils and segmented using Classification using Derivative-based Features. Multiple regression analysis was performed to examine the association between brain volume and various clinical and neuropsychiatric parameters adjusting for age, race, and sex. To evaluate longitudinal changes in brain volumes, a random coefficient model was used to evaluate the changes over time (age) adjusting for sex and race. RESULTS: The cross-sectional study included 164 PLWH and 51 controls, and the longitudinal study included 68 PLWH and 20 controls with 2 or more visits (mean 2.2 years, range 0.8-5.1 years). Gray matter (GM) atrophy rate was significantly higher in PLWH compared with control participants, and importantly, the GM and global atrophy was associated with the various neuropsychological domain scores. Higher volume of white matter hyperintensities were associated with increased atherosclerotic cardiovascular disease risk score, and decreased executive functioning and memory domain scores in PLWH. INTERPRETATION: These findings suggest ongoing neurological damage even in virologically controlled participants, with significant implications for clinical management of PLWH. ANN NEUROL 2024;95:941-950.

Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

OBJECTIVE: To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. METHODS: A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. RESULTS: We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. INTERPRETATION: GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024;96:276-288.

Systematic reduction of gray matter volume in anorexia nervosa, but relative enlargement with clinical symptoms in the prefrontal and posterior insular cortices: a multicenter neuroimaging study.

Although brain morphological abnormalities have been reported in anorexia nervosa (AN), the reliability and reproducibility of previous studies were limited due to insufficient sample sizes, which prevented exploratory analysis of the whole brain as opposed to regions of interest (ROIs). Objective was to identify brain morphological abnormalities in AN and the association with severity of AN by brain structural magnetic resonance imaging (MRI) in a multicenter study, and to conduct exploratory analysis of the whole brain. Here, we conducted a cross-sectional multicenter study using T1-weighted imaging (T1WI) data collected between May 2014 and February 2019 in Japan. We analyzed MRI data from 103 female AN patients (58 anorexia nervosa restricting type [ANR] and 45 anorexia nervosa binge-purging type [ANBP]) and 102 age-matched female healthy controls (HC). MRI data from five centers were preprocessed using the latest harmonization method to correct for intercenter differences. Gray matter volume (GMV) was calculated from T1WI data of all participants. Of the 205 participants, we obtained severity of eating disorder symptom scores from 179 participants, including 87 in the AN group (51 ANR, 36 ANBP) and 92 HC using the Eating Disorder Examination Questionnaire (EDE-Q) 6.0. GMV reduction were observed in the AN brain, including the bilateral cerebellum, middle and posterior cingulate gyrus, supplementary motor cortex, precentral gyrus medial segment, and thalamus. In addition, the orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC), rostral anterior cingulate cortex (ACC), and posterior insula volumes showed positive correlations with severity of symptoms. This multicenter study was conducted with a large sample size to identify brain morphological abnormalities in AN. The findings provide a better understanding of the pathogenesis of AN and have potential for the development of brain imaging biomarkers of AN. Trial Registration: UMIN000017456. https://center6.umin.ac.jp/cgi-open-bin/icdr/ctr_view.cgi?recptno=R000019303 .

Longitudinal associations of serum biomarkers with early cognitive, amyloid and grey matter changes.

Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-ß (Aß) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [n = 185, mean age (range) = 69 (53-84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aß1-42/Aß1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aß-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aß-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3-11.0) years]. Matching plasma measurements were available for Aß1-42/Aß1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (ßGFAP×Time = -0.021, PFDR = 0.007 and ßNfL×Time = -0.031, PFDR = 0.002) and language (ßGFAP×Time = -0.021, PFDR = 0.002 and ßNfL×Time = -0.018, PFDR = 0.03) domains. Low serum Aß1-42/Aß1-40 equally but independently predicted memory decline (ßAß1-42/Aß1-40×Time = -0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aß1-42/Aß1-40 predicted Aß accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aß1-42/Aß1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aß1-42/Aß1-40 decreased only in Aß-PET-negative elderly. NfL increases associated with declining memory (ßNfLchange×Time = -0.030, PFDR = 0.006) and language (ßNfLchange×Time = -0.021, PFDR = 0.02) function and serum Aß1-42/Aß1-40 decreases associated with declining language function (ßAß1-42/Aß1-40×Time = -0.020, PFDR = 0.04). GFAP increases associated with Aß accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aß accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aß1-42/Aß1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aß-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aß-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aß1-42/Aß1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner.

Prognostic value of single-subject grey matter networks in early multiple sclerosis.

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

A radiomics-based brain network in T1 images: construction, attributes, and applications.

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Effects of PRRT2 mutation on brain gray matter networks in paroxysmal kinesigenic dyskinesia.

Although proline-rich transmembrane protein 2 is the primary causative gene of paroxysmal kinesigenic dyskinesia, its effects on the brain structure of paroxysmal kinesigenic dyskinesia patients are not yet clear. Here, we explored the influence of proline-rich transmembrane protein 2 mutations on similarity-based gray matter morphological networks in individuals with paroxysmal kinesigenic dyskinesia. A total of 51 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations, 55 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, and 80 healthy controls participated in the study. We analyzed the structural connectome characteristics across groups by graph theory approaches. Relative to paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation and healthy controls, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations exhibited a notable increase in characteristic path length and a reduction in both global and local efficiency. Relative to healthy controls, both patient groups showed reduced nodal metrics in right postcentral gyrus, right angular, and bilateral thalamus; Relative to healthy controls and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations showed almost all reduced nodal centralities and structural connections in cortico-basal ganglia-thalamo-cortical circuit including bilateral supplementary motor area, bilateral pallidum, and right caudate nucleus. Finally, we used support vector machine by gray matter network matrices to classify paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, achieving an accuracy of 73%. These results show that proline-rich transmembrane protein 2 related gray matter network deficits may contribute to paroxysmal kinesigenic dyskinesia, offering new insights into its pathophysiological mechanisms.

Altered white matter functional pathways in Alzheimer's disease.

Alzheimer's disease (AD) is associated with functional disruption in gray matter (GM) and structural damage to white matter (WM), but the relationship to functional signal in WM is unknown. We performed the functional connectivity (FC) and graph theory analysis to investigate abnormalities of WM and GM functional networks and corpus callosum among different stages of AD from a publicly available dataset. Compared to the controls, AD group showed significantly decreased FC between the deep WM functional network (WM-FN) and the splenium of corpus callosum, between the sensorimotor/occipital WM-FN and GM visual network, but increased FC between the deep WM-FN and the GM sensorimotor network. In the clinical groups, the global assortativity, modular interaction between occipital WM-FN and visual network, nodal betweenness centrality, degree centrality, and nodal clustering coefficient in WM- and GM-FNs were reduced. However, modular interaction between deep WM-FN and sensorimotor network, and participation coefficients of deep WM-FN and splenium of corpus callosum were increased. These findings revealed the abnormal integration of functional networks in different stages of AD from a novel WM-FNs perspective. The abnormalities of WM functional pathways connect downward to the corpus callosum and upward to the GM are correlated with AD.

Neuropsychological insights into exercise addiction: the role of brain structure and self-efficacy in middle-older individuals.

This study aimed to investigate the relationship between exercise addiction and brain structure in middle-older individuals, and to examine the role of self-efficacy in mediating physiological changes associated with exercise addiction. A total of 133 patients exhibiting symptoms of exercise addiction were recruited for this study (male = 43, age 52.86 ± 11.78 years). Structural magnetic resonance imaging and behavioral assessments were administered to assess the study population. Voxel-based morphological analysis was conducted using SPM12 software. Mediation analysis was employed to explore the potential neuropsychological mechanism of self-efficacy in relation to exercise addiction. The findings revealed a positive correlation between exercise addiction and gray matter volume in the right inferior temporal region and the right hippocampus. Conversely, there was a negative correlation with gray matter volume in the left Rolandic operculum. Self-efficacy was found to indirectly influence exercise addiction by affecting right inferior temporal region gray matter volume and acted as a mediating variable in the relationship between the gray matter volume of right inferior temporal region and exercise addiction. In summary, this study elucidates the link between exercise addiction and brain structure among middle-older individuals. It uncovers the intricate interplay among exercise addiction, brain structure, and psychological factors. These findings enhance our comprehension of exercise addiction and offer valuable insights for the development of interventions and treatments.

The relationship between gray/white matter contrast and cognitive performance in first-episode schizophrenia.

Previous postmortem brain studies have revealed disturbed myelination in the intracortical regions in patients with schizophrenia, possibly reflecting anomalous brain maturational processes. However, it currently remains unclear whether this anomalous myelination is already present in early illness stages and/or progresses during the course of the illness. In this magnetic resonance imaging study, we examined gray/white matter contrast (GWC) as a potential marker of intracortical myelination in 63 first-episode schizophrenia (FESz) patients and 77 healthy controls (HC). Furthermore, we investigated the relationships between GWC findings and clinical/cognitive variables in FESz patients. GWC in the bilateral temporal, parietal, occipital, and insular regions was significantly higher in FESz patients than in HC, which was partly associated with the durations of illness and medication, the onset age, and lower executive and verbal learning performances. Because higher GWC implicates lower myelin in the deeper layers of the cortex, these results suggest that schizophrenia patients have less intracortical myelin at the time of their first psychotic episode, which underlies lower cognitive performance in early illness stages.

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population.

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Differential cortical gray matter changes in early- and late-onset patients with amyotrophic lateral sclerosis.

Age at onset may be an important feature associated with distinct subtypes of amyotrophic lateral sclerosis (ALS). Little is known about the neuropathological mechanism of early-onset ALS (EO-ALS) and late-onset ALS (LO-ALS). Ninety ALS patients were divided into EO-ALS and LO-ALS group, and 128 healthy controls were matched into young controls(YCs) and old controls (OCs). A voxel-based morphometry approach was employed to investigate differences in gray matter volume (GMV). Significant age at onset-by-diagnosis interactions were found in the left parietal operculum, left precentral gyrus, bilateral postcentral gyrus, right occipital gyrus, and right orbitofrontal cortex. Post hoc analysis revealed a significant decrease in GMV in all affected regions of EO-ALS patients compared with YCs, with increased GMV in 5 of the 6 brain regions, except for the right orbitofrontal cortex, in LO-ALS patients compared with OCs. LO-ALS patients had a significantly increased GMV than EO-ALS patients after removing the aging effect. Correspondingly, GMV of the left postcentral gyrus correlated with disease severity in the 2 ALS groups. Our findings suggested that the pathological mechanisms in ALS patients with different ages at onset might differ. These findings provide unique insight into the clinical and biological heterogeneity of the 2 ALS subtypes.