Brainnetome Atlas Research Articles

Biological mechanism of sex differences in mental rotation: Evidence from multimodal MRI, transcriptomic and receptor/transporter data

Sex differences in mental rotation are a well-documented phenomenon in cognitive research, with implications for the differing prevalence of neuropsychiatric disorders such as autism spectrum disorder (ASD), Alzheimer's disease (AD) and major depressive disorder (MDD) between the sexes. Despite extensive documentation, the biological mechanism underpinning these differences remain elusive. This study aimed to elucidate neural, genetic, and molecular bases of these disparities in mental rotation by integrating data from multimodal magnetic resonance imaging (MRI), transcriptomic and receptor/transporter. We first calculated the dynamic regional homogeneity (dReHo), gray matter volume (GMV) and fractional anisotropy (FA) in voxel-wise manner and parceled them into 246 brain regions based on Brainnetome Atlas. Subsequent analyses involved Pearson Correlations to examine the association between mental rotation performance and dReHo/GMV/FA and two-sample t-tests to delineate gender differences in these indices. Based on the above results, further mediation analysis was conducted to explore the relationship between sex, brain biomarkers and mental rotation. In addition, transcriptome-neuroimaging association analysis and correlation analysis between brain biomarkers and neurotransmitter receptor/transporter distribution were also performed to uncover genetic and molecular mechanisms contributing to the observed sex differences in mental rotation. We found correlations between mental rotation performance and dReHo, GMV and FA of the inferior parietal lobule (IPL) and superior temporal gyrus (STG) and sex effects on these brain biomarkers. Notably, the dReHo of the left IPL mediated the relationship between sex and mental rotation. Further correlation analysis revealed that the proton-coupled oligopeptide transporter PEPT2 (SLC15A2) and interleukin 17 receptor D (IL17RD) were associated with sex-related t-statistic maps and mental rotation-related r-statistic maps of dReHo. Moreover, γ-aminobutyric acid subtype A (GABAA) receptor availability was correlated with the r-statistic of dReHo, while norepinephrine transporter (NET) availability was correlated with its t-statistic. Serial mediation models revealed the indirect effect of these genes on the r-statistic maps through the transporter/receptor and t-statistic maps. Our findings provide novel insights into the biological mechanism underlying sex differences in mental rotation, identifying potential biomarkers for cognitive impairment and explaining variations in prevalence of certain mental disorders between the sexes. These results highlight the necessity of considering sex in research on mental health disorders.

Altered cingulate gyrus subregions functional connectivity in chronic insomnia disorder with anxiety

BackgroundChronic insomnia disorder (CID) is commonly associated with mood disorders. The cingulate gyrus (CG) plays a critical role in the pathophysiology of CID and anxiety. However, the specific characteristics of altered brain networks in the CG in CID with anxiety remain unclear. This study aimed to investigate the characteristics of CG functional connectivity (FC) in CID with and without anxiety. Methods: Resting-state functional magnetic resonance imaging was conducted on 92 CID and 36 healthy controls (HC). CID was divided into CID with anxiety (CID-A, N = 37) and CID without anxiety (CID-NA, N = 55) groups based on anxiety scores. Using the Human Brainnetome Atlas, the subregion CG FC network was constructed. ResultsCompared with HC, CID showed significantly decreased CG FC with the precuneus, middle frontal gyrus (MFG), and hippocampus, while showing significantly increased CG FC with the middle temporal gyrus (MTG)/superior temporal gyrus (STG). In contrast, CID-A showed significantly decreased CG FC with the salience network (insular, putamen) and default mode network (MTG/STG and inferior parietal lobule), while showing significantly increased CG FC with the thalamus and MFG compared to CID-NA. Further, CID-A and CID-NA could be classified with 84.21 % accuracy by using the CG FCs as features. Among these features, the CG FC with MFG, thalamus, and putamen had the highest contribution weights. ConclusionThis study revealed specific changes in the brain network of the CG subregion in CID-A. Understanding these CG FC alterations can help identify potential biomarkers specific to CID-A, which may be valuable for early detection and differentiation from other CID subtypes.

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury

BackgroundConvolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear. MethodsHere, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined. ResultsOur model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations. ConclusionWe proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates.

Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

A 3D pseudo-continuous arterial spin labeling study of altered cerebral blood flow correlation networks in mild cognitive impairment and Alzheimer's disease.

To investigate the abnormalities of the three-dimensional pseudo-continuous arterial spin labeling (3D PCASL) based cerebral blood flow (CBF) correlation networks in mild cognitive impairment (MCI) and Alzheimer's disease (AD). 3D PCASL images of 53 cognitive normal (CN) subjects, 43 subjects with MCI, and 30 subjects with AD were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Whole-brain CBF maps were calculated using PCASL and proton density-weighted images (PDWI). The 246 regional CBF values, including the cortex and subcortex, were obtained after registering the Brainnetome Atlas to the individual CBF maps. The Pearson correlation coefficient between every two regions across subjects was calculated to construct the CBF correlation network. Then the topologies of CBF networks with regard to global properties (global network efficiency, clustering coefficient, characteristic path length, and small-world properties), hub regions, nodal properties (betweenness centrality, BC), and connections were compared among CN, MCI, and AD. Significant changes in the global and nodal properties were observed in the permutation tests, and connections with significant differences survived after the z-statistic and false discovery rate (FDR) correction. The CBF correlation networks of CN, MCI, and AD all showed small-world properties. Compared with CN, global efficiency decreased significantly in AD. Significant differences in nodal properties and a loss of hub regions are noted in the middle temporal lobe in both MCI and AD. In the frontal lobe, BC is reduced in MCI while it is increased in the occipital lobe in AD. The identified altered hub regions with significant differences in MCI and AD were mainly distributed in the hippocampus and entorhinal cortex. In addition, disrupted hub regions in AD with significantly decreased connections were mainly found in the precuneus/posterior cingulate cortex (PCC) and hippocampus-cortical cortex. Noninvasive 3D PCASL-based CBF correlation networks are capable of showing changes in topological organization in subjects with MCI and AD, and the observed disruption in the topological organization may underlie cognitive decline in MCI and AD.

The non-decussating and decussating trigeminothalamic tracts in humans: A combination of connectome-based tractography and histological validation.

Functional anatomical research proposed the existence of a bilateral trigeminal ascending system although the anatomy trajectories of the trigeminothalamic connections cranial to the pons remain largely elusive. This study therefore aimed to clarify the anatomical distributions of the trigeminothalamic connections in humans. Advanced deterministic tractography to an averaged template of diffusion tensor imaging data from 1065 subjects from the Human Connectome Project was used. Seedings masks were placed in Montreal Neurological Institute standard space by use of the BigBrain histological dataset. Waypoint masks of the sensory thalamus was obtained from the Brainnetome Atlas. Tractography results were validated by use of the BigBrain histological dataset and Polarized Light Imaging microscopy. The trigeminothalamic tract bifurcated into a decussating ventral and a non-decussating dorsal tract. The ventral and dorsal tracts ascended to the contralateral thalamus and ipsilateral thalamus and reflected the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract, respectively. The projection of the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract to both thalami confirm the existence of a bilateral trigeminothalamic system in humans. Because our study is strictly anatomical, no further conclusions can be drawn with regard to physiological functionality. Future research should explore if the dorsal trigeminothalamic tract and the ventral trigeminothalamic tract actually transmit signals from noxious stimuli, this offers potential in understanding and possibly treating neuropathology in the orofacial region.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Functional connectivity changes of the hippocampal subregions in anti-N-methyl-D-aspartate receptor encephalitis.

The hippocampus plays an important role in the pathophysiological mechanism of Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis. Nevertheless, the connection between the resting-state activity of the hippocampal subregions and neuropsychiatric disorders in patients remains unclear. This study aimed to explore the changes in functional connectivity (FC) in the hippocampal subregions of patients with anti-NMDAR encephalitis and its association with clinical symptoms and cognitive performance. Twenty-three patients with anti-NMDAR encephalitis and 23 healthy controls (HC) were recruited. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans and completed clinical cognitive scales. Based on the Brainnetome Atlas, the rostral (anterior) and caudal (posterior) hippocampi of both the left and right hemispheres were selected as regions of interest (ROIs) for FC analysis. First, a one-sample t-test was used to observe the whole-brain connectivity distribution of hippocampal subregions within the patient and HC groups at a threshold of p < 0.05. The two-sample t-test was used to compare the differences in hippocampal ROIs connectivity between groups, followed by a partial correlation analysis between the FC values of brain regions with statistical differences and clinical variables. This study observed that the distribution of whole-brain functional connectivity in the rostral and caudal hippocampi aligned with the connectivity differences between the anterior and posterior hippocampi. Compared to the HC group, the patients showed significantly decreased FC between the bilateral rostral hippocampus and the left inferior orbitofrontal gyrus and between the right rostral hippocampus and the right cerebellum. However, a significant increase in FC was observed between the right rostral hippocampus and left superior temporal gyrus, the left caudal hippocampus and right superior frontal gyrus, and the right caudal hippocampus and left gyrus rectus. Partial correlation analysis showed that FC between the left inferior orbitofrontal gyrus and the right rostral hippocampus was significantly negatively correlated with the California Verbal Learning Test (CVLT) and Brief Visuospatial Memory Test (BVMT) scores. The FC between the right rostral hippocampus and the left superior temporal gyrus was negatively correlated with BVMT scores. FC abnormalities in the hippocampal subregions of patients with anti-NMDAR encephalitis were associated with cognitive impairment, emotional changes, and seizures. These results may help explain the pathophysiological mechanisms and clinical manifestations of anti-NMDAR encephalitis and NMDAR dysfunction-related diseases such as schizophrenia.

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.

Levels of neuronal pentraxin 2 in plasma is associated with cognitive function in patients with schizophrenia.

The precise diagnosis and treatment of cognitive impairment remains a major challenge in the field of schizophrenia (SCZ) research. Synaptic dysfunction and loss are thought to be closely related to the occurrence and development of SCZ and may be involved in cognitive dysfunction. The purpose of this study was to investigate whether neuronal pentraxins (NPTXs) plays a role in the etiology of SCZ and provide evidence of its possible therapeutic value a new target for drug development. We recruited 275 participants, of whom 148 were SCZ from psychiatric hospital and 127 healthy control (HC) subjects from communities. Plasma concentrations of NPTXs were measured in HC and SCZ at baseline and after 8 weeks of antipsychotic treatment. The MATRICS Cognitive Consensus Battery was used to evaluate cognitive function. Furthermore, the brain is parcellated into 246 subregions using the Brainnetome atlas, and we extracted regional white matter volumes from magnetic resonance images of the SCZ groups. Plasma NPTX2 levels were significantly lower in SCZ compared with HC subjects, but were significantly raised in SCZ after 8 weeks of antipsychotic treatment compared to baseline. In addition, baseline plasma NPTX2 levels were positively correlated with cognitive performance. These findings indicate that NPTX2 may reveal novel aspects of disease etiology and act as a promising target for new drug development.

Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data.

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.

Altered anterior insular metabolic connectivity in asymptomatic MAPT P301L carriers

AbstractBackgroundThe insula is the predominant brain region impaired in behavior variant frontotemporal dementia (bvFTD). However, structural and functional changes in the sub‐insula in the asymptomatic stage of bvFTD are unknown. The aim of this study is to describe structural and functional changes in insular subregions in asymptomatic carriers of the P301L mutation of the microtubule‐associated protein tau (MAPT) gene and patients with bvFTD.MethodSix asymptomatic MAPT P301L mutation carriers and 12 MAPT negative control subjects of the same pedigree were enrolled, along with 30 patients with a clinical diagnosis of bvFTD and 30 matched controls. All subjects underwent hybrid positron emission tomography/magnetic resonance imaging. Atlas‐based parcellation using a fine‐grained Brainnetome Atlas was conducted to assess gray matter (GM) volume, metabolism, and metabolic connectivity in the sub‐insula (region of interest).ResultThere was no significant GM atrophy or hypometabolism in insula subregions in asymptomatic MAPT P301L carriers, although decreased metabolic connectivity between vIa‐middle temporal gyrus, vIa‐temporal poles, dIa‐middle temporal gyrus and dIa‐temporal poles; and increased connectivity between vIa‐orbitofrontal, vIa‐dorsal lateral superior frontal gyrus, and dIa‐orbitofrontal and dIa‐dorsal lateral superior frontal gyrus were observed. Patients with bvFTD had significant atrophy and hypometabolism in all insula subregions and decreased metabolic connectivity in the whole brain, including vIa/dIa‐middle temporal and vIa/dIa‐temporal poles. The standardized uptake value ratios of vIa and dIa were negatively associated with behavioral disinhibition scale scores.ConclusionMetabolic connectivity is altered in vIa and dIa subregions of the sub‐insula in MAPT P301L mutation carriers before the occurrence of atrophy, hypometabolism, and clinical symptoms.

FC21: Loose functional connectivity within the striatum in behavioral variant frontotemporal dementia

Objective:Previous studies have reported that the structure and function of the striatum are important in bvFTD, and the striatum can be divided into more subregions. Changes within brain regions has recently attracted increasing attention, but most studies have explored the relationship between the striatum and other brain regions. Therefore, the aim of this study was to explore the changes in the intra-striatal resting-state functional connectivity (RSFC).Methods:We acquired fMRI data from 26 bvFTD patients and 36 healthy controls. The Human Brainnetome Atlas was used to define the spatial extent of the striatum and delineate its subregions. Intra- and extra-striatal FC values were then calculated for each individual and compared between bvFTD and control groups.Results:Compared to healthy controls, bvFTD showed decreased intra-striatal FC. Both intra-hemispheric and inter-hemispheric functional connectivity were compromised. There was also a gradient reduction in terms of the functional connectivity within striatum: the left dorsolateral putamen showed most decrease and the left ventral caudate exhibited the least (Fig 1). The extra-striatal FC between striatum and the insula was also decreased.Conclusion:The loose intra-striatal functional connectivity may underly the neural substrate of bvFTD.

Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study.

White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach. High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction. Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's. Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.

Alterations of functional connectivity in young people with depression mediate the relationship between sleep quality and cognitive function

BackgroundSleep disturbances is common in young people with depression, and poor sleep quality affects the ability to learn. In this study, we examined possible resting-state functional connectivity abnormalities between regions of interest, and clarified the relationship with depressive symptoms, sleep quality, and cognitive function. MethodsResting-state functional magnetic resonance imaging (fMRI) was collected on 42 healthy controls (HCs), 82 youth depressive patients (44 without sleep disturbances (NSD), and 38 with sleep disturbances (SD)). Regions of interest were defined by using Brainnetome Atlas. Functional connectivity was calculated, and its associations with depressive symptoms, sleep quality, and cognitive function were examined using correlation analysis and mediation analysis. ResultsThe left and right caudal of cingulate gyrus, tongue and larynx region of postcentral gyrus were significant brain regions in NSD versus SD. The average functional connectivity between these regions was associated with poor sleep quality (r = 0.368, p = 0.001) and worse working memory (r = −0.256, p = 0.023) and mediated the relationship between sleep quality and working memory (c = −0.738, c' = −0.500). LimitationData consistency in this study was not good enough. This study did not monitor sleep rhythms to provide objective sleep-related data. ConclusionThe functional connectivity between the left and right caudal of cingulate gyrus with tongue and larynx region of postcentral gyrus may be the neural mechanism by which sleep disturbances affect working memory. This provides an intervention target for clinically improving cognitive function in young people with depression.

Altered Anterior Insular Metabolic Connectivity in Asymptomatic MAPT P301L Carriers.

The insula is the predominant brain region impaired in behavior variant frontotemporal dementia (bvFTD). However, structural and functional changes in the sub-insula in the asymptomatic stage of bvFTD are unknown. To describe structural and functional changes in insula subregions in asymptomatic carriers of the P301L mutation of the microtubule-associated protein tau (MAPT) gene and patients with bvFTD. Six asymptomatic MAPT P301L mutation carriers and 12 MAPT negative control subjects of the same pedigree were enrolled, along with 30 patients with a clinical diagnosis of bvFTD and 30 matched controls. All subjects underwent hybrid positron emission tomography/magnetic resonance imaging. Atlas-based parcellation using a fine-grained Brainnetome Atlas was conducted to assess gray matter (GM) volume, metabolism, and metabolic connectivity in the sub-insula (region of interest). There was no significant GM atrophy or hypometabolism in insula subregions in asymptomatic MAPT P301L carriers, although decreased metabolic connectivity between vIa-middle temporal gyrus, vIa-temporal poles, dIa-middle temporal gyrus and dIa-temporal poles; and increased connectivity between vIa-orbitofrontal, vIa-dorsal lateral superior frontal gyrus, and dIa-orbitofrontal and dIa-dorsal lateral superior frontal gyrus were observed. Patients with bvFTD had significant atrophy and hypometabolism in all insula subregions and decreased metabolic connectivity in the whole brain, including vIa/dIa-middle temporal and vIa/dIa-temporal poles. The standardized uptake value ratios of vIa and dIa were negatively associated with behavioral disinhibition scale scores. Metabolic connectivity is altered in vIa and dIa subregions of the sub-insula in MAPT P301L mutation carriers before the occurrence of atrophy, hypometabolism, and clinical symptoms.

Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network.

Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.

Amygdala Perfusion Is Associated with Persistent Affective Symptoms after Sports-Related Concussion (P8-1.003)

<h3>Objective:</h3> We evaluated PASL-based amygdala perfusion and symptom recovery after sports-related concussion (SRC). <h3>Background:</h3> Perfusion changes are well-documented in the pathophysiology of SRC. We have previously shown that rsfMRI-BOLD amygdala hyperconnectivity was associated with decreased acute symptoms, but increased chronic symptoms. We hypothesized that amygdala perfusion changes might correlate with symptoms after SRC and partly explain BOLD-connectivity findings. <h3>Design/Methods:</h3> We calculated PASL-based perfusion (mL/100g/min) at 3 timepoints after SRC (T1=1–4days, T2=10–14days, T3=2–3months) in collegiate athletes (N=31, female=14), as well as in-sport (ISC; N=29, female=15), and non-contact (NCC; N=35, female=14) controls. We assessed between- and within-group differences in four amygdala ROIs from the Brainnetome Atlas using ANOVA and Tukey’s HSD. Subject-level PASL values were correlated with SCAT-3 Graded Symptom Checklist (GSC) and Brief Symptom Inventory 18 (BSI-18) scores at each timepoint. <h3>Results:</h3> L medial and lateral amygdala (mAmy/lAmy) perfusion was elevated in SRC subjects compared to controls at T1, returning to control-level by T2 and T3. Among SRC subjects at T1, R lAmy perfusion positively correlated with BSI-18 depression subdomain; perfusion in all 4 amygdala ROIs positively correlated with BSI-18 anxiety subdomain and BSI-18 symptom total. Among ISC subjects at T1, R mAmy perfusion negatively correlated with GSC symptom total, GSC physical subdomain, and GSC cognitive subdomain; among NCC subjects, L mAmy perfusion negatively correlated with BSI-18 depression subdomain. Among SRC subjects at T2, L lAmy, L mAmy, and R lAmy perfusion positively correlated with BSI-18 depression subdomain, BSI-18 anxiety subdomain, and BSI-18 severity score. Among SRC subjects at T3, L lAmy perfusion positively correlated with all three BSI-18 subdomains and BSI-18 severity score. <h3>Conclusions:</h3> Increased amygdala perfusion after SRC positively correlated with affective symptoms; persistently elevated L lAmy perfusion was associated with increased chronic symptoms. Accounting for perfusion in analysis of BOLD-connectivity of the amygdala after SRC may improve prediction of affective symptom persistence. <b>Disclosure:</b> Mr. Sridharan has nothing to disclose. Mr. Sheridan has nothing to disclose. Mr. Hudson has nothing to disclose. Dr. Lipford has nothing to disclose. Dr. Bickart has nothing to disclose. Dr. Thibeault has received personal compensation for serving as an employee of NovaSignal Corp.. Dr. Thibeault has received stock or an ownership interest from NovaSignal Inc.. Dr. Giza has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Highmark Interactive. Dr. Giza has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Novartis Pharmaceutical. Dr. Giza has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Lewitt, Hackman, Shapiro, Marshall &amp; Harlan. Dr. Giza has received stock or an ownership interest from Highmark Interactive. The institution of Dr. Giza has received research support from NCAA. The institution of Dr. Giza has received research support from US Department of Defense. The institution of Dr. Giza has received research support from UCLA: Brain Injury Research Center, Steve Tisch BrainSPORT Program, Easton Clinic for Brain Health. Dr. Giza has received publishing royalties from a publication relating to health care. Dr. Hamilton has received personal compensation for serving as an employee of NovaSignal Corp . Dr. Hamilton has received stock or an ownership interest from NovaSignal Corp. Dr. Hamilton has received intellectual property interests from a discovery or technology relating to health care. Dr. Choe has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Legal Cases. The institution of Dr. Choe has received research support from NIH. The institution of Dr. Choe has received research support from AIR-P. Dr. Choe has a non-compensated relationship as a Board of Directors with NABIS that is relevant to AAN interests or activities. Dr. Choe has a non-compensated relationship as a Consultant with USA Swimming Concussion Task Force that is relevant to AAN interests or activities. Dr. Whitlow has nothing to disclose.

Extra-Basal Ganglia Brain Structures Are Related to Motor Reserve in Parkinson's Disease.

The "motor reserve" is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. We investigated brain structures relevant to the motor reserve in Parkinson's disease (PD) in this study. Patients with drug-naïve, early PD were enrolled, who then underwent dopamine transporter (DAT) scan and diffusion tensor imaging (DTI). The severity of motor symptoms was evaluated with the Unified Parkinson's Disease Rating Scale score of bradykinesia and rigidity on the more affected side and dopaminergic degeneration of DAT uptake of the more affected putamen. Individual motor reserve estimate (MRE) was evaluated based on the discrepancy between the severity of motor symptoms and dopaminergic degeneration. Using DTI and the Brainnetome atlas, brain structures correlated with MRE were identified. We enrolled 193 patients with drug-naïve PD (mean disease duration of 15.6±13.2 months), and the MRE successfully predicted the increase of levodopa equivalent dose after two years. In the DTI analysis, fractional anisotropy values of medial, inferior frontal, and temporal lobes, limbic structures, nucleus accumbens, and thalamus were positively correlated with the MRE, while no brain structures were correlated with mean diffusivity. Additionally, degree centrality derived from the structural connectivity of the frontal and temporal lobes and limbic structures was positively correlated with the MRE. Our results show empirical evidence for MR in PD and brain structures relevant to MR, particularly, the extra-basal ganglia system including the limbic and frontal structures.

Functional network modularity and efficiency supports episodic memory in older adults with amyloid‐beta pathology

AbstractBackgroundEpisodic memory is one of the first cognitive domains to decline in preclinical Alzheimer’s disease. We investigated whether graph metrics of functional networks, specifically modularity and local efficiency, are related to episodic memory and amyloid‐β (Aβ) positivity in cognitively normal older adults.MethodSixty‐five cognitively normal older adults (mean age 72.3 ± 6.2 years; 62% female) underwent high‐resolution resting state fMRI (1.8mm3, partial acquisition) to measure functional connectivity, 18F‐Florbetapir‐PET to measure Aβ pathology, and cognitive assessment. Resting state fMRI data was processed with CONN. ROIs (n=135) from the Brainnetome Atlas contained within rsfMRI coverage were used to generate ROI‐to‐ROI functional connectivity matrices. Graph measures of modularity (Q) and local efficiency were computed with the Brain Connectivity Toolbox using binary graphs at costs ranging from 5%‐50% of network sparsity (5% step size). Aβ status was determined using a &gt;1.11 SUVR threshold of a composite region spanning the association cortex, resulting in 32% of the sample being classified as Aβ+. Episodic memory was measured using the immediate recall measure of the Rey Auditory Verbal Learning Test (RAVLT A5).ResultAβ+ participants had significantly increased modularity compared to Aβ‐ (Fig1A, p&lt;0.05 for costs 25 and 50), but no differences in local efficiency (Fig1B). There was no significant effect of Aβ positivity on RAVLT A5 performance (t(63)=‐0.67, p = 0.51). However, there were significant interactions between Aβ status and RAVLT A5 with both modularity and local efficiency at most cost functions (Fig2). Better RAVLT A5 performance was associated with higher modularity (Fig2A; ex. cost 25: r = 0.54, p=0.01) and higher local efficiency (Fig2B; ex. cost 25: r = 0.63, p=0.002) within Aβ+ participants, but not Aβ‐ participants (ps&gt;0.36).ConclusionOur findings suggest that increases in functional network modularity and local efficiency within Aβ+ cognitively normal older adults may act as a compensatory mechanism to maintain equivalent levels of episodic memory performance.