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

Individual Differences in Hemispheric Emotional Valence by Computerized Test Correlate with Lateralized Differences in Nucleus Accumbens, Hippocampal and Amygdala Volumes

Purpose

Conventional theories of hemispheric emotional valence (HEV) postulate fixed hemispheric differences in emotional processing. Schiffer’s dual brain psychology proposes that there are prominent individual differences with a substantial subset showing a reversed laterality pattern. He further proposed that hemispheric differences were more akin to differences in personality than in emotional processing. This theory is supported by findings that unilateral treatments, such as transcranial magnetic stimulation, are effective if they accurately target individual differences in laterality. The aim of this paper was to assess if a computer test of hemispheric emotional valence (CTHEV) could effectively identify individual differences in HEV and to ascertain if these individual differences were associated with underlying differences in brain structure and connectivity.

Patients and Methods

The CTHEV was administered to 50 (18 male/32 female) right-handed participants, aged 18–19 years, enrolled in a study assessing the neurobiological effects of childhood maltreatment. Based on a literature review, we determined whether CTHEV correlated with lateralized volumes of the nucleus accumbens, amygdala, hippocampus, and subgenual anterior cingulate as well as volume of the corpus callosum.

Results

CTHEV scores correlated with laterality indices of the nucleus accumbens (p = 0.00016), amygdala (p = 0.0138) and hippocampus (p = 0.031). A positive left hemispheric valence was associated with a larger left-sided nucleus accumbens and hippocampus and a smaller left amygdala. We identified four eigenvector network centrality DTI measures that predict CTHEV, most notably the left amygdala, and found that CTHEV results correlated with total and segment-specific corpus callosal volumes.

Conclusion

Individual differences in HEV can be readily assessed by computer test and correlate with differences in brain structure and connectivity that could provide a mechanistic understanding. These findings provide further support for a revised understanding of HEV and provide a tool that could be used to guide lateralized brain treatments.

Combining MRI and cognitive evaluation to classify concussion in university athletes

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R² = 0.32, p < .001), and performed worse on tests of concentration (∆R² = 0.07, p < .05) and delayed memory (∆R² = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

Microstructural alterations predict impaired bimanual control in Parkinson’s disease

Bimanual coordination is impaired in Parkinson’s disease affecting patients’ ability to perform activities of daily living and to maintain independence. Conveyance of information between cortical and subcortical areas is essential for bimanual coordination and relies on the integrity of cerebral microstructure. As pathological deposition of alpha-synuclein compromises microstructure in Parkinson’s disease, we investigated the relationship between microstructural integrity and bimanual coordination using diffusion-weighted MRI in 23 patients with Parkinson’s disease (mean age ± standard deviation: 56.0 ± 6.45 years; 8 female) and 26 older adults (mean age ± standard deviation: 58.5 ± 5.52 years). Whole-brain analysis revealed specific microstructural alterations between patients and healthy controls matched for age, sex, handedness, and cognitive status congruent with the literature and known Parkinson’s disease pathology. A general linear model revealed distinct microstructural alterations associated with poor bimanual coordination in Parkinson’s disease, corrected for multiple comparisons using a permutation-based approach. Integrating known functional topography, we conclude that distinct changes in microstructure cause an impediment of structures involved in attention, working memory, executive function, motor planning, motor control, and visual processing contributing to impaired bimanual coordination in Parkinson’s disease.

Association of DNA Methylation of the NLRP3 Gene with Changes in Cortical Thickness in Major Depressive Disorder

The Nod-like receptor pyrin containing 3 (NLRP3) inflammasome has been reported to be a convergent point linking the peripheral immune response induced by psychological stress and neuroinflammatory processes in the brain. We aimed to identify differences in the methylation profiles of the NLRP3 gene between major depressive disorder (MDD) patients and healthy controls (HCs). We also investigated the correlation of the methylation score of loci in NLRP3 with cortical thickness in the MDD group using magnetic resonance imaging (MRI) data. A total of 220 patients with MDD and 82 HCs were included in the study, and genome-wide DNA methylation profiling of the NLRP3 gene was performed. Among the total sample, 88 patients with MDD and 74 HCs underwent T1-weighted structural MRI and were included in the neuroimaging–methylation analysis. We identified five significant differentially methylated positions (DMPs) in NLRP3. In the MDD group, the methylation scores of cg18793688 and cg09418290 showed significant positive or negative correlations with cortical thickness in the occipital, parietal, temporal, and frontal regions, which showed significant differences in cortical thickness between the MDD and HC groups. Our findings suggest that NLRP3 DNA methylation may predispose to depression-related brain structural changes by increasing NLRP3 inflammasome-related neuroinflammatory processes in MDD.

SCN1A overexpression, associated with a genomic region marked by a risk variant for a common epilepsy, raises seizure susceptibility

Mesial temporal lobe epilepsy with hippocampal sclerosis and a history of febrile seizures is associated with common variation at rs7587026, located in the promoter region of SCN1A. We sought to explore possible underlying mechanisms. SCN1A expression was analysed in hippocampal biopsy specimens of individuals with mesial temporal lobe epilepsy with hippocampal sclerosis who underwent surgical treatment, and hippocampal neuronal cell loss was quantitatively assessed using immunohistochemistry. In healthy individuals, hippocampal volume was measured using MRI. Analyses were performed stratified by rs7587026 type. To study the functional consequences of increased SCN1A expression, we generated, using transposon-mediated bacterial artificial chromosome transgenesis, a zebrafish line expressing exogenous scn1a, and performed EEG analysis on larval optic tecta at 4 day post-fertilization. Finally, we used an in vitro promoter analysis to study whether the genetic motif containing rs7587026 influences promoter activity. Hippocampal SCN1A expression differed by rs7587026 genotype (Kruskal-Wallis test P = 0.004). Individuals homozygous for the minor allele showed significantly increased expression compared to those homozygous for the major allele (Dunn's test P = 0.003), and to heterozygotes (Dunn's test P = 0.035). No statistically significant differences in hippocampal neuronal cell loss were observed between the three genotypes. Among 597 healthy participants, individuals homozygous for the minor allele at rs7587026 displayed significantly reduced mean hippocampal volume compared to major allele homozygotes (Cohen's D = - 0.28, P = 0.02), and to heterozygotes (Cohen's D = - 0.36, P = 0.009). Compared to wild type, scn1lab-overexpressing zebrafish larvae exhibited more frequent spontaneous seizures [one-way ANOVA F(4,54) = 6.95 (P < 0.001)]. The number of EEG discharges correlated with the level of scn1lab overexpression [one-way ANOVA F(4,15) = 10.75 (P < 0.001]. Finally, we showed that a 50 bp promoter motif containing rs7587026 exerts a strong regulatory role on SCN1A expression, though we could not directly link this to rs7587026 itself. Our results develop the mechanistic link between rs7587026 and mesial temporal lobe epilepsy with hippocampal sclerosis and a history of febrile seizures. Furthermore, we propose that quantitative precision may be important when increasing SCN1A expression in current strategies aiming to treat seizures in conditions involving SCN1A haploinsufficiency, such as Dravet syndrome.

Neurostructural differences associated with self-harm in youth bipolar disorder

BACKGROUND

Youth with bipolar disorder (BD) are at greatly elevated risk for suicide. Self-harm, encompassing all self-injurious behaviors regardless of suicidal intent, is among one of the greatest risk factors for death by suicide. This study aims to extend the sparse literature regarding the neurostructural correlates of self-harm in youth with BD.

METHODS

Participants included 156 youth (17.14 ± 1.61 years): 38 BD with lifetime history of self-harm (BD_SH+ ), 43 BD without history of self-harm (BD_SH- ), and 75 healthy controls (HC). Measures of cortical thickness, surface area (SA), and volume were obtained using 3 T magnetic resonance imaging. Orbitofrontal and ventrolateral prefrontal cortices were examined in region-of-interest (ROI) analyses, complemented by exploratory vertex-wise analyses using a general linear model controlling for age, sex, and intracranial volume.

RESULTS

In ROI analyses, there were no between-group differences after correction for multiple comparisons. Vertex-wise analysis revealed three significant clusters in precentral gyrus SA, inferior temporal gyrus SA, and caudal middle frontal gyrus volume. Post-hoc vertex-wise analyses showed BD_SH+ had lower cortical SA and volume compared with both BD_SH- and HC for all clusters.

CONCLUSIONS

Significant vertex-wise findings were observed in frontotemporal regions relevant to BD and self-harm, with smaller neurostructural measures among BD_SH+ compared with both BD_SH- and HC. Future studies are needed to evaluate the temporal nature of the relationship of these neurostructural differences (i.e., potential risk indicators) to self-harm and to identify mechanisms underlying these findings.

A Transformer-based Multi-features Fusion Model for Prediction of Conversion in Mild Cognitive Impairment

Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins

Schizophrenia is characterized by abnormal brain structure such as global reductions in gray matter volume. Machine learning models trained to estimate the age of brains from structural neuroimaging data consistently show advanced brain-age to be associated with schizophrenia. Yet, it is unclear whether advanced brain-age is specific to schizophrenia compared to other psychotic disorders, and whether evidence that brain structure is "older" than chronological age actually reflects neurodevelopmental rather than atrophic processes. It is also unknown whether advanced brain-age is associated with genetic liability for psychosis carried by biological relatives of people with schizophrenia. We used the Brain-Age Regression Analysis and Computation Utility Software (BARACUS) prediction model and calculated the residualized brain-age gap of 332 adults (163 individuals with psychotic disorders: 105 schizophrenia, 17 schizoaffective disorder, 41 bipolar I disorder with psychotic features; 103 first-degree biological relatives; 66 controls). The model estimated advanced brain-ages for people with psychosis in comparison to controls and relatives, with no differences among psychotic disorders or between relatives and controls. Specifically, the model revealed an enlarged brain-age gap for schizophrenia and bipolar disorder with psychotic features. Advanced brain-age was associated with lower cognitive and general functioning in the full sample. Among relatives, cognitive performance and schizotypal symptoms were related to brain-age gap, suggesting that advanced brain-age is associated with the subtle expressions associated with psychosis. Exploratory longitudinal analyses suggested that brain aging was not accelerated in individuals with a psychotic disorder. In sum, we found that people with psychotic disorders, irrespective of specific diagnosis or illness severity, show indications of non-progressive, advanced brain-age. These findings support a transdiagnostic, neurodevelopmental formulation of structural brain abnormalities in psychotic psychopathology.

Accelerated longitudinal cortical atrophy in OEF / OIF / OND veterans with severe PTSD and the impact of comorbid TBI

Veterans who deployed in support of Operation Enduring Freedom (OEF), Iraqi Freedom (OIF), and New Dawn (OND) commonly experience severe psychological trauma, often accompanied by physical brain trauma resulting in mild traumatic brain injury (mTBI). Prior studies of individuals with posttraumatic stress disorder (PTSD) have revealed alterations in brain structure, accelerated cellular aging, and impacts on cognition following exposure to severe psychological trauma and potential interactive effects of military‐related mTBI. To date, however, little is known how such deployment‐related trauma changes with time and age of injury of the affected veteran. In this study, we explored changes in cortical thickness, volume, and surface area after an average interval of approximately 2 years in a cohort of 254 OEF/OIF/OND Veterans ranging in age from 19 to 67 years. Whole‐brain vertex‐wise analyses revealed that veterans who met criteria for severe PTSD (Clinician‐Administered PTSD Scale ≥60) at baseline showed greater negative longitudinal changes in cortical thickness, volume, and area over time. Analyses also revealed a significant severe‐PTSD by age interaction on cortical measures with severe‐PTSD individuals exhibiting accelerated cortical degeneration with increasing age. Interaction effects of comorbid military‐related mTBI within the severe‐PTSD group were also observed in several cortical regions. These results suggest that those exhibiting severe PTSD symptomatology have accelerated atrophy that is exacerbated with increasing age and history of mTBI.

Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females

Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.

Montreal Cognitive Assessment (MoCA) performance in Huntington's disease patients correlates with cortical and caudate atrophy

Huntington's Disease (HD) is an autosomal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. Cognitive impairment develops gradually in HD patients, progressing later into a severe cognitive dysfunction. The Montreal Cognitive Assessment (MoCA) is a brief screening test commonly employed to detect mild cognitive impairment, which has also been useful to assess cognitive decline in HD patients. However, the relationship between MoCA performance and brain structural integrity in HD patients remains unclear. Therefore, to explore this relationship we analyzed if cortical thinning and subcortical nuclei volume differences correlated with HD patients' MoCA performance. Twenty-two HD patients and twenty-two healthy subjects participated in this study. T1-weighted images were acquired to analyze cortical thickness and subcortical nuclei volumes. Group comparison analysis showed a significantly lower score in the MoCA global performance of HD patients. Also, the MoCA total score correlated with cortical thinning of fronto-parietal and temporo-occipital cortices, as well as with bilateral caudate volume differences in HD patients. These results provide new insights into the effectiveness of using the MoCA test to detect cognitive impairment and the brain atrophy pattern associated with the cognitive status of prodromal/early HD patients.

Semi-automated Segmentation and Quantification of Perivascular Spaces at 7 Tesla in COVID-19

While COVID-19 is primarily considered a respiratory disease, it has been shown to affect the central nervous system. Mounting evidence shows that COVID-19 is associated with neurological complications as well as effects thought to be related to neuroinflammatory processes. Due to the novelty of COVID-19, there is a need to better understand the possible long-term effects it may have on patients, particularly linkage to neuroinflammatory processes. Perivascular spaces (PVS) are small fluid-filled spaces in the brain that appear on MRI scans near blood vessels and are believed to play a role in modulation of the immune response, leukocyte trafficking, and glymphatic drainage. Some studies have suggested that increased number or presence of PVS could be considered a marker of increased blood-brain barrier permeability or dysfunction and may be involved in or precede cascades leading to neuroinflammatory processes. Due to their size, PVS are better detected on MRI at ultrahigh magnetic field strengths such as 7 Tesla, with improved sensitivity and resolution to quantify both concentration and size. As such, the objective of this prospective study was to leverage a semi-automated detection tool to identify and quantify differences in perivascular spaces between a group of 10 COVID-19 patients and a similar subset of controls to determine whether PVS might be biomarkers of COVID-19-mediated neuroinflammation. Results demonstrate a detectable difference in neuroinflammatory measures in the patient group compared to controls. PVS count and white matter volume were significantly different in the patient group compared to controls, yet there was no significant association between PVS count and symptom measures. Our findings suggest that the PVS count may be a viable marker for neuroinflammation in COVID-19, and other diseases which may be linked to neuroinflammatory processes.

Beta-amyloid moderates the relationship between cortical thickness and attentional control in middle- and older-aged adults

Although often unmeasured in studies of cognition, many older adults possess Alzheimer disease (AD) pathologies such as beta-amyloid (Aβ) deposition, despite being asymptomatic. We were interested in examining whether the behavior-structure relationship observed in later life was altered by the presence of preclinical AD pathology. A total of 511 cognitively unimpaired adults completed magnetic resonance imaging and three attentional control tasks; a subset (n = 396) also underwent Aβ-positron emissions tomography. A vertex-wise model was conducted to spatially represent the relationship between cortical thickness and average attentional control accuracy, while moderation analysis examined whether Aβ deposition impacted this relationship. First, we found that reduced cortical thickness in temporal, medial- and lateral-parietal, and dorsolateral prefrontal cortex, predicted worse performance on the attention task composite. Subsequent moderation analyses observed that levels of Aβ significantly influence the relationship between cortical thickness and attentional control. Our results support the hypothesis that preclinical AD, as measured by Aβ deposition, is partially driving what would otherwise be considered general aging in a cognitively normal adult population.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry

This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

Musicianship-Related Structural and Functional Cortical Features Are Preserved in Elderly Musicians

Background

Professional musicians are a model population for exploring basic auditory function, sensorimotor and multisensory integration, and training-induced neuroplasticity. The brain of musicians exhibits distinct structural and functional cortical features; however, little is known about how these features evolve during aging. This multiparametric study aimed to examine the functional and structural neural correlates of lifelong musical practice in elderly professional musicians.

Methods

Sixteen young musicians, 16 elderly musicians (age >70), and 15 elderly non-musicians participated in the study. We assessed gray matter metrics at the whole-brain and region of interest (ROI) levels using high-resolution magnetic resonance imaging (MRI) with the Freesurfer automatic segmentation and reconstruction pipeline. We used BrainVoyager semiautomated segmentation to explore individual auditory cortex morphotypes. Furthermore, we evaluated functional blood oxygenation level-dependent (BOLD) activations in auditory and non-auditory regions by functional MRI (fMRI) with an attentive tone-listening task. Finally, we performed discriminant function analyses based on structural and functional ROIs.

Results

A general reduction of gray matter metrics distinguished the elderly from the young subjects at the whole-brain level, corresponding to widespread natural brain atrophy. Age- and musicianship-dependent structural correlations revealed group-specific differences in several clusters including superior, middle, and inferior frontal as well as perirolandic areas. In addition, the elderly musicians exhibited increased gyrification of auditory cortex like the young musicians. During fMRI, the elderly non-musicians activated predominantly auditory regions, whereas the elderly musicians co-activated a much broader network of auditory association areas, primary and secondary motor areas, and prefrontal and parietal regions like, albeit weaker, the young musicians. Also, group-specific age- and musicianship-dependent functional correlations were observed in the frontal and parietal regions. Moreover, discriminant function analysis could separate groups with high accuracy based on a set of specific structural and functional, mainly temporal and occipital, ROIs.

Conclusion

In conclusion, despite naturally occurring senescence, the elderly musicians maintained musicianship-specific structural and functional cortical features. The identified structural and functional brain regions, discriminating elderly musicians from non-musicians, might be of relevance for the aging musicians’ brain. To what extent lifelong musical activity may have a neuroprotective impact needs to be addressed further in larger longitudinal studies.

Investigating the contribution of white matter hyperintensities and cortical thickness to empathy in neurodegenerative and cerebrovascular diseases

Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases. Five hundred thirteen participants with Alzheimer's disease/mild cognitive impairment, amyotrophic lateral sclerosis, frontotemporal dementia (FTD), Parkinson's disease, or cerebrovascular disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness. A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex (female), global cognition, and right parietal and occipital WMH. Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

Beyond body maps: Information content of specific body parts is distributed across the somatosensory homunculus

The homunculus in primary somatosensory cortex (S1) is famous for its body part selectivity, but this dominant feature may eclipse other representational features, e.g., information content, also relevant for S1 organization. Using multivariate fMRI analysis, we ask whether body part information content can be identified in S1 beyond its primary region. Throughout S1, we identify significant representational dissimilarities between body parts but also subparts in distant non-primary regions (e.g., between the hand and the lips in the foot region and between different face parts in the foot region). Two movements performed by one body part (e.g., the hand) could also be dissociated well beyond its primary region (e.g., in the foot and face regions), even within Brodmann area 3b. Our results demonstrate that information content is more distributed across S1 than selectivity maps suggest. This finding reveals underlying information contents in S1 that could be harnessed for rehabilitation and brain-machine interfaces.

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We replicate the high univariate selectivity profile of the somatosensory homunculus

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We use multivariate fMRI analysis to identify information content beyond selectivity

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Significant body part and action-related content are found throughout the homunculus

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Functional information is available, even in regions selective to other body parts

Midsagittal corpus callosal thickness and cognitive impairment in Parkinson's disease

People diagnosed with Parkinson's disease (PD) can experience significant neuropsychiatric symptoms, including cognitive impairment and dementia, the neuroanatomical substrates of which are not fully characterised. Symptoms associated with cognitive impairment and dementia in PD may relate to direct structural changes to the corpus callosum via primary white matter pathology, or as a secondary outcome due to the degeneration of cortical regions. Using magnetic resonance imaging, the corpus callosum can be investigated at the midsagittal plane, where it converges to a contiguous mass and is not intertwined with other tracts. The objective of this project was thus twofold; first, we investigated possible changes in the thickness of the midsagittal callosum and cortex in patients with PD with varying levels of cognitive impairment; and secondly, we investigated the relationship between the thickness of the midsagittal corpus callosum and the thickness of the cortex. Study participants included cognitively unimpaired PD participants (n = 35), PD participants with mild cognitive impairment (n = 22), PD participants with dementia (n = 17) and healthy controls (n = 27). We found thinning of the callosum in PD-related dementia compared to PD-related mild cognitive impairment and cognitively unimpaired PD participants. Regression analyses found thickness of the left medial orbitofrontal cortex to be positively correlated with thickness of the anterior callosum in PD-related mild cognitive impairment. This study suggests that a midsagittal thickness model can uncover changes to the corpus callosum in PD-related dementia, which occur in line with changes to the cortex in this advanced disease stage.

Associations between neonatal hypoglycaemia and brain volumes, cortical thickness and white matter microstructure in mid-childhood: An MRI study

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Neonatal hypoglycaemia is associated with damage to the brain in the acute phase.

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In mid-childhood, neonatal hypoglycaemia is associated with smaller brain regions.

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Deep grey matter regions such as the caudate and thalamus are implicated.

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Children with neonatal hypoglycemia had smaller occipital lobe cortical thickness.

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Grey matter may be especially vulnerable to long-term effects of neonatal hypoglycemia.

Neonatal hypoglycaemia is a common metabolic disorder that may cause brain damage, most visible in parieto-occipital regions on MRI in the acute phase. However, the long term effects of neonatal hypoglycaemia on the brain are not well understood. We investigated the association between neonatal hypoglycaemia and brain volumes, cortical thickness and white matter microstructure at 9–10 years.

Children born at risk of neonatal hypoglycaemia at ≥ 36 weeks’ gestation who took part in a prospective cohort study underwent brain MRI at 9–10 years. Neonatal hypoglycaemia was defined as at least one hypoglycaemic episode (at least one consecutive blood glucose concentration < 2.6 mmol/L) or interstitial episode (at least 10 min of interstitial glucose concentrations < 2.6 mmol/L). Brain volumes and cortical thickness were computed using Freesurfer. White matter microstructure was assessed using tract-based spatial statistics.

Children who had (n = 75) and had not (n = 26) experienced neonatal hypoglycaemia had similar combined parietal and occipital lobe volumes and no differences in white matter microstructure at nine years of age. However, those who had experienced neonatal hypoglycaemia had smaller caudate volumes (mean difference: −557 mm³, 95% confidence interval (CI), −933 to −182, p = 0.004) and smaller thalamus (−0.03%, 95%CI, −0.06 to 0.00; p = 0.05) and subcortical grey matter (−0.10%, 95%CI −0.20 to 0.00, p = 0.05) volumes as percentage of total brain volume, and thinner occipital lobe cortex (−0.05 mm, 95%CI −0.10 to 0.00, p = 0.05) than those who had not. The finding of smaller caudate volumes after neonatal hypoglycaemia was consistent across analyses of pre-specified severity groups, clinically detected hypoglycaemic episodes, and severity and frequency of hypoglycaemic events.

Neonatal hypoglycaemia is associated with smaller deep grey matter brain regions and thinner occipital lobe cortex but not altered white matter microstructure in mid-childhood.

Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90-98% and 65-70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.

Association of blood pressure with brain structure in youth with and without bipolar disorder

BACKGROUND

We previously found that blood pressure (BP) is elevated, and associated with poorer neurocognition, in youth with bipolar disorder (BD). While higher BP is associated with smaller brain structure in adults, studies have not examined this topic in BD or youth.

METHODS

Participants were 154 youth, ages 13-20 (n = 81 BD, n = 73 HC). Structural magnetic resonance imaging and diastolic (DBP), and systolic (SBP) pressure were obtained. Region of interest (ROI; anterior cingulate cortex [ACC], insular cortex, hippocampus) and vertex-wise analyses controlling for age, sex, body-mass-index, and intracranial volume investigated BP-neurostructural associations; a group-by-BP interaction was also assessed.

RESULTS

In ROI analyses, higher DBP in the overall sample was associated with smaller insular cortex area (β=-0.18 p = 0.007) and was associated with smaller ACC area to a significantly greater extent in HC vs. BD (β=-0.14 p = 0.015). In vertex-wise analyses, higher DBP and SBP were associated with smaller area and volume in the insular cortex, frontal, parietal, and temporal regions in the overall sample. Additionally, higher SBP was associated with greater thickness in temporal and parietal regions. Finally, higher SBP was associated with smaller area and volume in frontal, parietal, and temporal regions to a significantly greater extent in BD vs. HC.

LIMITATIONS

Cross-sectional design, single assessment of BP.

CONCLUSION

BP is associated with brain structure in youth, with variability related to structural phenotype (volume vs. thickness) and psychiatric diagnosis (BD vs. HC). Future studies evaluating temporality of these findings, and the association of BP changes on brain structure in youth, are warranted.

Association between the Estimated Glomerular Filtration Rate and Brain Atrophy in Older Adults

End-stage kidney disease has been associated with cognitive impairment and brain atrophy. It remains unclear if mild to moderate kidney dysfunction is associated with brain atrophy, especially in older adults. We used cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), an NIH-funded multicenter longitudinal cohort study, to better understand the association between estimated glomerular filtration rate (eGFR) and brain volumes. We included all ADNI participants with both baseline serum creatinine values and MRI brain volume assessments. We used multiple linear regression modeling to assess cross-sectional associations between eGFR and whole-brain gray matter, hippocampus, entorhinal, fusiform, and middle temporal brain volumes. Participants (n = 1,596) were 74 ± 7 years old with a mean eGFR of 69.4 ± 14.8 mL/min/1.73 m2; 53% had mild cognitive impairment, and 19% had dementia. Unadjusted analysis showed an association between lower eGFR and smaller brain volumes. After adjusting for age, sex, and education, there was no association between eGFR brain volumes (p > 0.05 for all). These results remained consistent after subgroup analysis by age stratification and baseline cognitive status. Age was a confounding variable in the unadjusted association between the eGFR and brain volumes. Thus, a mild to moderately reduced eGFR was not associated with brain atrophy in ADNI participants.

Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder

The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and many theories recognize as mechanisms of autism a disruption of brain development and maturation time course, suggesting the existence of common neurobiological substrates, such as defective synaptic structure and aberrant brain connectivity. Magnetic resonance imaging (MRI) plays an important role in both assessment of region-specific structural changes and quantification of specific alterations in gray or white matter, which could lead to the identification of an MRI biomarker. In this study, we performed measurement of cortical thickness in a selected well-known group of preschool ASD subjects with the aim of finding correlation between cortical metrics and clinical scores to understand the underlying mechanism of symptoms and to support early clinical diagnosis. Our results confirm that recent brain MRI techniques combined with clinical data can provide some useful information in defining the cerebral regions involved in ASD although large sample studies with homogeneous analytical and multisite approaches are needed.

Longitudinal grey matter development associated with psychotic experiences in young people

Background

Gray matter abnormalities are observed across the psychosis spectrum. The trajectory of these abnormalities in healthy adolescents reporting subthreshold psychotic experiences (PEs) may provide insight into the neural mechanisms underlying psychotic symptoms. The risk of psychosis and additional psychopathology is even higher among these individuals who also report childhood adversity/DSM-5 diagnoses. Thus, the aims of this longitudinal study were to investigate PE-related volumetric changes in young people, noting any effects of childhood adversity/DSM-5 diagnosis.

Methods

A total of 211 young people 11 to 13 years of age participated in the initial Adolescent Brain Development study. PE classification was determined by expert consensus at each time point. Participants underwent neuroimaging at 3 time points over 6 years. A total of 76 participants with at least one scan were included in the final sample; 34 who met criteria for PEs at least once across all the time points (PE group) and 42 control subjects. Data from 20 bilateral regions of interest were extracted for linear mixed-effects analyses.

Results

Right hippocampal volume increased over time in the control group, with no increase in the PE group (p = .00352). DSM-5 diagnosis and childhood adversity were not significantly associated with right hippocampal volume. There was no significant effect of group or interaction in any other region.

Conclusions

These findings further implicate right hippocampal volumetric abnormalities in the pathophysiology underlying PEs. Furthermore, as suggested by previous studies in those at clinical high risk for psychosis and those with first-episode psychosis, it is possible that these deficits may be a marker for later clinical outcomes.

Diversity of daily activities is associated with greater hippocampal volume

Greater engagement in a range of daily activities is associated with better cognitive functioning (Lee et al., Lee et al., 2020). The hippocampus, a subcortical brain structure implicated in learning, memory, spatial navigation and other aspects of cognitive functioning, may be structurally sensitive to exposure to and engagement with novel experiences and environments. The present study tested whether greater activity diversity, defined as the range of common daily activities engaged in and the proportion of time spent in each, is associated with larger hippocampal volume. Greater diversity of activities, as measured using daily diaries across an 8-day period, was related to greater hippocampal volume averaged across the left and right hemispheres, even when adjusting for estimated intracranial volume, total activity time, sociodemographic factors, and self-reported physical health. These findings are broadly consistent with nonhuman animal studies, demonstrating a link between enriched environments and structural changes to the hippocampus. Future longitudinal and experimental work can elucidate causal and directional relationships between diversity of daily activities and hippocampal volume.

Heart rate and heart rate variability in patients with chronic inflammatory joint disease: the role of pain duration and the insular cortex

Background

Chronic inflammatory joint diseases (CIJD) have been linked to increased cardiovascular morbidity and mortality. A decisive reason could be a dysregulation of the autonomic nervous system, which is responsible for the control of cardiovascular function. So far, the cause of changes in autonomic nervous system functions remains elusive. In this study, we investigate the role of chronic pain and the insular cortex in autonomic control of cardiac functioning in patients with CIJD.

Methods

We studied the autonomic nervous system through the assessment of heart rate and heart rate variability (HRV) at rest and under cognitive stimulation. Furthermore, we investigated insular cortex volume by performing surface-based brain morphometry with FreeSurfer. For this study, 47 participants were recruited, 22 individual age- and sex-matched pairs for the magnetic resonance imaging analyses and 14 for the HRV analyses. All available patients’ data were used for analysis.

Results

Pain duration was negatively correlated with the resting heart rate in patients with chronic inflammatory joint diseases (n = 20). In a multiple linear regression model including only CIJD patients with heart rate at rest as a dependent variable, we found a significant positive relationship between heart rate at rest and the volume of the left insular cortex and a significant negative relationship between heart rate at rest and the volume of the right insular cortex. However, we found no significant differences in HRV parameters or insular cortex volumes between both groups.

Conclusions

In this study we provide evidence to suggest insular cortex involvement in the process of ANS changes due to chronic pain in CIJD patients.

The study was preregistered with the German Clinical Trials Register (https://www.drks.de; DRKS00012791; date of registration: 28 July 2017).

Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Aging-Related Changes in Cortical Sources of Sleep Oscillatory Neural Activity Following Motor Learning Reflect Contributions of Cortical Thickness and Pre-sleep Functional Activity

Oscillatory neural activity during sleep, such as that in the delta and sigma bands, is important for motor learning consolidation. This activity is reduced with typical aging, and this reduction may contribute to aging-related declines in motor learning consolidation. Evidence suggests that brain regions involved in motor learning contribute to oscillatory neural activity during subsequent sleep. However, aging-related differences in regional contributions to sleep oscillatory activity following motor learning are unclear. To characterize these differences, we estimated the cortical sources of consolidation-related oscillatory activity using individual anatomical information in young and older adults during non-rapid eye movement sleep after motor learning and analyzed them in light of cortical thickness and pre-sleep functional brain activation. High-density electroencephalogram was recorded from young and older adults during a midday nap, following completion of a functional magnetic resonance imaged serial reaction time task as part of a larger experimental protocol. Sleep delta activity was reduced with age in a left-weighted motor cortical network, including premotor cortex, primary motor cortex, supplementary motor area, and pre-supplementary motor area, as well as non-motor regions in parietal, temporal, occipital, and cingulate cortices. Sleep theta activity was reduced with age in a similar left-weighted motor network, and in non-motor prefrontal and middle cingulate regions. Sleep sigma activity was reduced with age in left primary motor cortex, in a non-motor right-weighted prefrontal-temporal network, and in cingulate regions. Cortical thinning mediated aging-related sigma reductions in lateral orbitofrontal cortex and frontal pole, and partially mediated delta reductions in parahippocampal, fusiform, and lingual gyri. Putamen, caudate, and inferior parietal cortex activation prior to sleep predicted frontal and motor cortical contributions to sleep delta and theta activity in an age-moderated fashion, reflecting negative relationships in young adults and positive or absent relationships in older adults. Overall, these results support the local sleep hypothesis that brain regions active during learning contribute to consolidation-related neural activity during subsequent sleep and demonstrate that sleep oscillatory activity in these regions is reduced with aging.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Sex Differences in Brain and Cognition in de novo Parkinson's Disease

Background and Objective: Brain atrophy and cognitive impairment in neurodegenerative diseases are influenced by sex. We aimed to investigate sex differences in brain atrophy and cognition in de novo Parkinson's disease (PD) patients. Methods: Clinical, neuropsychological and T1-weighted MRI data from 205 PD patients (127 males: 78 females) and 69 healthy controls (40 males: 29 females) were obtained from the PPMI dataset. Results: PD males had a greater motor and rapid eye movement sleep behavior disorder symptomatology than PD females. They also showed cortical thinning in postcentral and precentral regions, greater global cortical and subcortical atrophy and smaller volumes in thalamus, caudate, putamen, pallidum, hippocampus, and brainstem, compared with PD females. Healthy controls only showed reduced hippocampal volume in males compared to females. PD males performed worse than PD females in global cognition, immediate verbal recall, and mental processing speed. In both groups males performed worse than females in semantic verbal fluency and delayed verbal recall; as well as females performed worse than males in visuospatial function. Conclusions: Sex effect in brain and cognition is already evident in de novo PD not explained by age per se, being a relevant factor to consider in clinical and translational research in PD.

Pre- and Post-therapy Risk Factors for Vasculopathy in Pediatric Craniopharyngioma Patients Treated with Surgery and Proton Radiotherapy

PURPOSE

Vasculopathy (VAS) is a significant complication associated with radiotherapy in patients treated for brain tumors. We studied the type, location, severity, timing, and resolution of VAS in children with craniopharyngioma treated with proton radiotherapy (PRT) and evaluated predictors of stenosis (STN) using a novel patient and imaging-based modelling approach.

MATERIALS/METHODS

Children with craniopharyngioma (N=94) were treated with 54 GyRBE PRT on a clinical trial NCTXXXXXXXX.¹ We evaluated VAS type, location, severity, and resolution. VAS events were segmented and related to their location, operative corridor, PRT dose, and vascular territory to facilitate Mixed Effect Logistic Regression Modelling of spatial predictors of STN events.

RESULTS

Forty-five (47.9%) patients had 111 instances of confirmed VAS (pre-PRT N = 37, 33.3%). The median time to post-PRT VAS was 3.41 years (95% CI 1.86-6.11). Stenosis events were observed post-PRT in 23.4% (N=22) patients. Post-PRT VAS was detected by cerebral angiogram in 9.6% (N=9), severe in 4.3% (N=4), and compensated on perfusion in 2.1% (N=2). Revascularization was required for 5 (5.3%) patients. Post-surgical, pre-PRT VAS, and PRT dose to unperturbed vessels were predictive of STN. The impact of PRT on STN was negligible within the surgical corridor.

CONCLUSIONS

VAS often precedes PRT and was the strongest predictor of post-PRT STN. The adverse impact of PRT on STN was only apparent in unperturbed vasculature beyond the operative corridor.

Interaction of childhood abuse and depressive symptoms on cortical thickness: a general population study

Childhood abuse was inconsistently related to whole-brain cortical thickness in former studies. However, both childhood abuse and cortical thickness have been associated with depressive symptoms. We hypothesised that childhood abuse moderates the association between depressive symptoms and cortical thickness. In 1551 individuals of the general population, associations between whole-brain cortical thickness and the interaction of childhood abuse (emotional, physical, and sexual) and depressive symptoms were analysed using an ANCOVA. Linear regression analyses were used to estimate the same effect on the cortical thickness of 34 separate regions (Desikan-Killiany-atlas). A significant interaction effect of childhood abuse and depressive symptoms was observed for whole-brain cortical thickness (F(2, 1534) = 5.28, p = 0.007). A thinner cortex was associated with depressive symptoms in abused (t value = 2.78, p = 0.025) but not in non-abused participants (t value = - 1.50, p = 0.224). Focussing on non-depressed participants, a thicker whole-brain cortex was found in abused compared to non-abused participants (t value = - 2.79, p = 0.025). Similar interaction effects were observed in 12 out of 34 cortical regions. Our results suggest that childhood abuse is associated with reduced cortical thickness in subjects with depressive symptoms. In abused subjects without depressive symptoms, larger cortical thickness might act compensatory and thus reflect resilience against depressive symptoms.

Dual feature correlation guided multi-task learning for Alzheimer's disease prediction

Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting cognition functions. Predicting the cognitive scores from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of AD. Despite machine learning algorithms having many successful applications, the prediction model suffers from the so-called curse of dimensionality. Multi-task feature learning (MTFL) has helped tackle this problem incorporating the correlations among multiple clinical cognitive scores. However, MTFL neglects the inherent correlation among brain imaging measures. In order to better predict the cognitive scores and identify stable biomarkers, we first propose a generalized multi-task formulation framework that incorporates the task and feature correlation structures simultaneously. Second, we present a novel feature-aware sparsity-inducing norm (FAS-norm) penalty to incorporate a useful correlation between brain regions by exploiting correlations among features. Three multi-task learning models that incorporate the FAS-norm penalty are proposed following our framework. Finally, the algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the non-smooth problems. We comprehensively evaluate the proposed models on the cross-sectional and longitudinal Alzheimer's disease neuroimaging initiative datasets. The inputs are the thickness measures and the volume measures of the cortical regions of interest. Compared with MTFL, our methods achieve an average decrease of 4.28% in overall error in the cross-sectional analysis and an average decrease of 7.97% in the Alzheimer's Disease Assessment Scale cognitive total score longitudinal analysis. Moreover, our methods identify sensitive and stable biomarkers to physicians, such as the hippocampus, lateral ventricle, and corpus callosum.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes

BACKGROUND

Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

OBJECTIVE

To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

METHODS

We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.

RESULTS

For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.

CONCLUSION

Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Neural Correlates of Traumatic Brain Injury in Women Survivors of Intimate Partner Violence: A Structural and Functional Connectivity Neuroimaging Study

OBJECTIVE

More than one-third of women in the United States experience intimate partner violence (IPV) in their lifetime, increasing their risk for traumatic brain injury (TBI). Despite the prevalence of TBI among IPV survivors, research is sparse in comparison with parallel populations (eg, military, accidents, sports). This pilot study aimed to provide a preliminary investigation of the effect of TBI on brain morphometry and resting-state functional connectivity in women who experience IPV.

PARTICIPANTS

A total of 45 community-dwelling women survivors of IPV who screened positive for posttraumatic stress disorder (PTSD).

DESIGN

Participants completed comprehensive assessments of trauma exposure, PTSD, TBI history, and brain neurological health. Twenty-three participants (51.1%) met diagnostic criteria for lifetime TBI. Of these, 15 participants experienced 1 or more TBIs resulting from IPV. The remaining participants experienced TBI from non-IPV exposures (eg, sports/motor vehicle accident). Surface-based neuroimaging analyses were performed to examine group differences in cortical thickness and in functional connectivity of amygdala and isthmus cingulate seeds to examine emotion regulation and the default mode network, respectively.

MAIN MEASURES

Boston Assessment of Traumatic Brain Injury-Lifetime for Intimate Partner Violence (BAT-L/IPV); Clinician Administered PTSD Scale (CAPS); structural and functional neuroimaging.

RESULTS

History of lifetime TBI in women IPV survivors was associated with differences in cortical thickness as well as functional connectivity between the isthmus cingulate seed and a variety of regions, including superior parietal and frontal cortices. Individuals with IPV-related TBI showed lower cortical thickness in the right paracentral gyrus than individuals with TBI from other non-IPV etiologies.

CONCLUSION

Significant differences in brain structure and connectivity were observed in individuals with IPV and TBI. A lower mean cortical thickness of the paracentral gyrus was associated with TBI due to IPV than TBI from other etiologies. Although preliminary, findings from this pilot study present a step toward identifying potential mechanisms by which IPV and TBI secondary to IPV impact brain health in women.

Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

Body mass index but not genetic risk is longitudinally associated with altered structural brain parameters

Evidence from previous studies suggests that elevated body mass index (BMI) and genetic risk for obesity is associated with reduced brain volume, particularly in areas of reward-related cognition, e.g. the medial prefrontal cortex (AC-MPFC), the orbitofrontal cortex (OFC), the striatum and the thalamus. However, only few studies examined the interplay between these factors in a joint approach. Moreover, previous findings are based on cross-sectional data. We investigated the longitudinal relationship between increased BMI, brain structural magnetic resonance imaging (MRI) parameters and genetic risk scores in a cohort of n = 502 community-dwelling participants from the Study of Health in Pomerania (SHIP) with a mean follow-up-time of 4.9 years. We found that (1) increased BMI values at baseline were associated with decreased brain parameters at follow-up. These effects were particularly pronounced for the OFC and AC-MPFC. (2) The genetic predisposition for BMI had no effect on brain parameters at baseline or follow-up. (3) The interaction between the genetic score for BMI and brain parameters had no effect on BMI at baseline. Finding a significant impact of overweight, but not genetic predisposition for obesity on altered brain structure suggests that metabolic mechanisms may underlie the relationship between obesity and altered brain structure.

Longitudinal Changes in Cortical Thickness in Adolescents with Autism Spectrum Disorder and Their Association with Restricted and Repetitive Behaviors

The neuroanatomy of autism spectrum disorder (ASD) shows highly heterogeneous developmental trajectories across individuals. Mapping atypical brain development onto clinical phenotypes, and establishing their molecular underpinnings, is therefore crucial for patient stratification and subtyping. In this longitudinal study we examined intra- and inter-individual differences in the developmental trajectory of cortical thickness (CT) in childhood and adolescence, and their genomic underpinnings, in 33 individuals with ASD and 37 typically developing controls (aged 11–18 years). Moreover, we aimed to link regional atypical CT development to intra-individual variations in restricted and repetitive behavior (RRB) over a two-year time period. Individuals with ASD showed significantly reduced cortical thinning in several of the brain regions functionally related to wider autism symptoms and traits (e.g., fronto-temporal and cingulate cortices). The spatial patterns of the neuroanatomical differences in CT were enriched for genes known to be associated with ASD at a genetic and transcriptomic level. Further, intra-individual differences in CT correlated with within-subject variability in the severity of RRBs. Our findings represent an important step towards characterizing the neuroanatomical underpinnings of ASD across development based upon measures of CT. Moreover, our findings provide important novel insights into the link between microscopic and macroscopic pathology in ASD, as well as their relationship with different clinical ASD phenotypes.

Effects of Meditation on Structural Changes of the Brain in Patients With Mild Cognitive Impairment or Alzheimer's Disease Dementia

Previous cross-sectional studies reported positive effects of meditation on the brain areas related to attention and executive function in the healthy elderly population. Effects of long-term regular meditation in persons with mild cognitive impairment (MCI) and Alzheimer's disease dementia (AD) have rarely been studied. In this study, we explored changes in cortical thickness and gray matter volume in meditation-naïve persons with MCI or mild AD after long-term meditation intervention. MCI or mild AD patients underwent detailed clinical and neuropsychological assessment and were assigned into meditation or non-meditation groups. High resolution T1-weighted magnetic resonance images (MRI) were acquired at baseline and after 6 months. Longitudinal symmetrized percentage changes (SPC) in cortical thickness and gray matter volume were estimated. Left caudal middle frontal, left rostral middle frontal, left superior parietal, right lateral orbitofrontal, and right superior frontal cortices showed changes in both cortical thickness and gray matter volume; the left paracentral cortex showed changes in cortical thickness; the left lateral occipital, left superior frontal, left banks of the superior temporal sulcus (bankssts), and left medial orbitofrontal cortices showed changes in gray matter volume. All these areas exhibited significantly higher SPC values in meditators as compared to non-meditators. Conversely, the left lateral occipital, and right posterior cingulate cortices showed significantly lower SPC values for cortical thickness in the meditators. In hippocampal subfields analysis, we observed significantly higher SPC in gray matter volume of the left CA1, molecular layer HP, and CA3 with a trend for increased gray matter volume in most other areas. No significant changes were found for the hippocampal subfields in the right hemisphere. Analysis of the subcortical structures revealed significantly increased volume in the right thalamus in the meditation group. The results of the study point out that long-term meditation practice in persons with MCI or mild AD leads to salutary changes in cortical thickness and gray matter volumes. Most of these changes were observed in the brain areas related to executive control and memory that are prominently at risk in neurodegenerative diseases.

Eccentric rehabilitation induces white matter plasticity and sensorimotor recovery in chronic spinal cord injury

Experience-dependent white matter plasticity offers new potential for rehabilitation-induced recovery after neurotrauma. This first-in-human translational experiment combined myelin water imaging in humans and genetic fate-mapping of oligodendrocyte lineage cells in mice to investigate whether downhill locomotor rehabilitation that emphasizes eccentric muscle actions promotes white matter plasticity and recovery in chronic, incomplete spinal cord injury (SCI). In humans, of 20 individuals with SCI that enrolled, four passed the imaging screen and had myelin water imaging before and after a 12-week (3 times/week) downhill locomotor treadmill training program (SCI + DH). One individual was excluded for imaging artifacts. Uninjured control participants (n = 7) had two myelin water imaging sessions within the same day. Changes in myelin water fraction (MWF), a histopathologically-validated myelin biomarker, were analyzed in a priori motor learning and non-motor learning brain regions and the cervical spinal cord using statistical approaches appropriate for small sample sizes. PDGFRα-CreER^T2:mT/mG mice, that express green fluorescent protein on oligodendrocyte precursor cells and subsequent newly-differentiated oligodendrocytes upon tamoxifen-induced recombination, were either naive (n = 6) or received a moderate (75 kilodyne), contusive SCI at T9 and were randomized to downhill training (n = 6) or unexercised groups (n = 6). We initiated recombination 29 days post-injury, seven days prior to downhill training. Mice underwent two weeks of daily downhill training on the same 10% decline grade used in humans. Between-group comparison of functional (motor and sensory) and histological (oligodendrogenesis, oligodendroglial/axon interaction, paranodal structure) outcomes occurred post-training. In humans with SCI, downhill training increased MWF in brain motor learning regions (postcentral, precuneus) and mixed motor and sensory tracts of the ventral cervical spinal cord compared to control participants (P < 0.05). In mice with thoracic SCI, downhill training induced oligodendrogenesis in cervical dorsal and lateral white matter, increased axon-oligodendroglial interactions, and normalized paranodal structure in dorsal column sensory tracts (P < 0.05). Downhill training improved sensorimotor recovery in mice by normalizing hip and knee motor control and reducing hyperalgesia, both of which were associated with new oligodendrocytes in the cervical dorsal columns (P < 0.05). Our findings indicate that eccentric-focused, downhill rehabilitation promotes white matter plasticity and improved function in chronic SCI, likely via oligodendrogenesis in nervous system regions activated by the training paradigm. Together, these data reveal an exciting role for eccentric training in white matter plasticity and sensorimotor recovery after SCI.

Structural-covariance networks identify topology-based cortical-thickness changes in children with persistent executive function impairments after traumatic brain injury

Paediatric traumatic brain injury (pTBI) results in inconsistent changes to regional morphometry of the brain across studies. Structural-covariance networks represent the degree to which the morphology (typically cortical-thickness) of cortical-regions co-varies with other regions, driven by both biological and developmental factors. Understanding how heterogeneous regional changes may influence wider cortical network organization may more appropriately capture prognostic information in terms of long term outcome following a pTBI. The current study aimed to investigate the relationships between cortical organisation as measured by structural-covariance, and long-term cognitive impairment following pTBI. T1-weighted magnetic resonance imaging (MRI) from n = 83 pTBI patients and 33 typically developing controls underwent 3D-tissue segmentation using Freesurfer to estimate cortical-thickness across 68 cortical ROIs. Structural-covariance between regions was estimated using Pearson's correlations between cortical-thickness measures across 68 regions-of-interest (ROIs), generating a group-level 68 × 68 adjacency matrix for patients and controls. We grouped a subset of patients who underwent executive function testing at 2-years post-injury using a neuropsychological impairment (NPI) rule, defining impaired- and non-impaired subgroups. Despite finding no significant reductions in regional cortical-thickness between the control and pTBI groups, we found specific reductions in graph-level strength of the structural covariance graph only between controls and the pTBI group with executive function (EF) impairment. Node-level differences in strength for this group were primarily found in frontal regions. We also investigated whether the top n nodes in terms of effect-size of cortical-thickness reductions were nodes that had significantly greater strength in the typically developing brain than n randomly selected regions. We found that acute cortical-thickness reductions post-pTBI are loaded onto regions typically high in structural covariance. This association was found in those patients with persistent EF impairment at 2-years post-injury, but not in those for whom these abilities were spared. This study posits that the topography of post-injury cortical-thickness reductions in regions that are central to the typical structural-covariance topology of the brain, can explain which patients have poor EF at follow-up.

Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder

Bipolar disorder (BD) is associated with a 20-30-fold increased suicide risk compared to the general population. First-degree relatives of BD patients show inflated rates of psychopathology including suicidal behaviors. As reliable biomarkers of suicide attempts (SA) are lacking, we examined associations between suicide-related polygenic risk scores (PRSs)-a quantitative index of genomic risk-and variability in brain structures implicated in SA. Participants (n = 206; aged 12-30 years) were unrelated individuals of European ancestry and comprised three groups: 41 BD cases, 96 BD relatives ("high risk"), and 69 controls. Genotyping employed PsychArray, followed by imputation. Three PRSs were computed using genome-wide association data for SA in BD (SA-in-BD), SA in major depressive disorder (SA-in-MDD) (Mullins et al., 2019, The American Journal of Psychiatry, 176(8), 651-660), and risky behavior (Karlsson Linnér et al., 2019, Nature Genetics, 51(2), 245-257). Structural magnetic resonance imaging processing employed FreeSurfer v5.3.0. General linear models were constructed using 32 regions-of-interest identified from suicide neuroimaging literature, with false-discovery-rate correction. SA-in-MDD and SA-in-BD PRSs negatively predicted parahippocampal thickness, with the latter association modified by group membership. SA-in-BD and Risky Behavior PRSs inversely predicted rostral and caudal anterior cingulate structure, respectively, with the latter effect driven by the "high risk" group. SA-in-MDD and SA-in-BD PRSs positively predicted cuneus structure, irrespective of group. This study demonstrated associations between PRSs for suicide-related phenotypes and structural variability in brain regions implicated in SA. Future exploration of extended PRSs, in conjunction with a range of biological, phenotypic, environmental, and experiential data in high risk populations, may inform predictive models for suicidal behaviors.

A population receptive field model of the magnetoencephalography response

Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

Morphometry and functional connectivity of auditory cortex in school-age children with profound language disabilities: Five comparative case studies

Many neurodevelopmental conditions imply absent or severely reduced language capacities at school age. Evidence from functional magnetic resonance imaging is highly limited. We selected a series of five cases scanned with the same fMRI paradigm and the aim of relating individual language profiles onto underlying patterns of functional connectivity (FC) across auditory language cortex: three with neurogenetic syndromes (Coffin-Siris, Landau-Kleffner, and Fragile-X), one with idiopathic intellectual disability, one with autism spectrum disorder (ASD). Compared to both a group with typical development (TD) and a verbal ASD group (total N = 110), they all showed interhemispheric FC below two standard deviations of the TD mean. Children with higher language scores showed higher intrahemispheric FC between Heschl's gyrus and other auditory language regions, as well as an increase of FC during language stimulation compared to rest. An increase of FC in forward vs. reversed speech in the posterior and middle temporal gyri was seen across all cases. The Coffin-Siris case, the most severe, also had the most anomalous FC patterns and showed reduced myelin content, while the Landau-Kleffner case showed reduced cortical thickness. These results suggest potential for neural markers and mechanisms of severe language processing deficits under highly heterogeneous etiological conditions.

Anatomically Standardized Detection of MRI Atrophy Patterns in Early-Stage Alzheimer's Disease

MRI studies have consistently identified atrophy patterns in Alzheimer’s disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.