Cortical cartography and Caret software

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Development and evaluation of a panel of newly screened Y chromosome InDels for inferring paternal ancestry information in Southwest China

Y-InDels (insertions/deletions) are genetic markers which are extremely understudied. It is unknown whether this type of markers can be utilized for genetic ancestry inference. We have developed an innovative Y chromosome ancestry inference system tailored for forensic applications. This panel amplifies 21 Y chromosome loci, encompassing Y-InDels and Y-SNPs (Single Nucleotide Polymorphism), utilizing the capillary electrophoresis (CE) platform. The system performed well at DNA concentrations greater than 0.125 ng/ul and produced accurate results at a 1:100 mixing ratio of male and female DNA. The Cumulative probability of matching (CPM) was between 0.95 and 0.97 in the experimental population. The system's efficacy in inferring ancestral origins was demonstrated through intercontinental population discrimination, revealing high discrimination power between African and East Asian populations. Population genetic analyses conducted on Han, Qiang and Hui populations in Southwest China, where the smallest FST value was 0.0002 between Han Chinese in Beijing (from 1000 Genomes Project) and Qiang Chinese from Sichuan (CQSC). Phylogenetic tree construction further illuminated distinct haplotypes among populations, with ethnically unique haplotypes observed in 34.6% of Hui and 7.1% of Qiang populations. K-fold cross-validation show the system's inference abilities at the intercontinental level. In addition, our investigations identified potential associations between the Y-InDel locus Y: 15,385,547 (GRCh37) and haplogroup R1a1a1b2a2- Z2124, as well as locus Y: 13,990,180 (GRCh37) and haplogroup F-M89. In conclusion, we have established a Y-chromosome inference system tailored for grassroots-level application, underscoring the value of incorporating Y-InDel markers in forensic analyses.

Defining Trajectories of Linguistic, Cognitive-Communicative, and Quality of Life Outcomes in Aphasia: Longitudinal Observational Study Protocol

Objective

To describe the trajectories of linguistic, cognitive-communicative, and health-related quality of life (HRQOL) outcomes after stroke in persons with aphasia.

Design

Longitudinal observational study from inpatient rehabilitation to 18 months after stroke.

Setting

Four US mid-west inpatient rehabilitation facilities (IRFs).

Participants

We plan to recruit 400 adult (older than 21 years) English speakers who meet the following inclusion criteria: (1) Diagnosis of aphasia after a left-hemisphere infarct confirmed by CT scan or magnetic resonance imaging (MRI); (2) first admission for inpatient rehabilitation due to a neurologic event; and (3) sufficient cognitive capacity to provide informed consent and participate in testing. Exclusion criteria include any neurologic condition other than stroke that could affect language, cognition or speech, such as Parkinson's disease, Alzheimer's disease, traumatic brain injury, or the presence of right-hemisphere lesions.

Interventions

Not applicable.

Main Outcome Measures

Subjects are administered a test battery of linguistic, cognitive-communicative, and HRQOL measures. Linguistic measures include the Western Aphasia Battery-Revised and the Apraxia of Speech Rating Scale. Cognitive-communicative measures include the Communication Participation Item Bank, Connor's Continuous Performance Test-3, the Communication Confidence Rating Scale for Aphasia, the Communication Effectiveness Index, the Neurological Quality of Life measurement system (Neuro-QoL) Communication short form, and the Neuro-QoL Cognitive Function short form. HRQOL measures include the 39-item Stroke & Aphasia Quality of Life Scale, Neuro-QoL Fatigue, Sleep Disturbance, Depression, Ability to Participate in Social Roles & Activities, and Satisfaction with Social Roles & Activities tests, and the Patient-Reported Outcome Measurement and Information System 10-item Global Health short form. The test battery is administered initially during inpatient rehabilitation, and at 3-, 6-, 12-, and 18-months post-IRF discharge. Biomarker samples are collected via saliva samples at admission and a subgroup of participants also undergo resting state fMRI scans.

Results

Not applicable.

Conclusions

This longitudinal observational study will develop trajectory models for recovery of clinically relevant linguistic, cognitive-communicative, and quality of life outcomes over 18 months after inpatient rehabilitation. Models will identify individual differences in the patterns of recovery based on variations in personal, genetic, imaging, and therapy characteristics. The resulting models will provide an unparalleled representation of recovery from aphasia resulting from stroke. This improved understanding of recovery will enable clinicians to better tailor and plan rehabilitation therapies to individual patient's needs.

A ubiquitous spectrolaminar motif of local field potential power across the primate cortex

The mammalian cerebral cortex is anatomically organized into a six-layer motif. It is currently unknown whether a corresponding laminar motif of neuronal activity patterns exists across the cortex. Here we report such a motif in the power of local field potentials (LFPs). Using laminar probes, we recorded LFPs from 14 cortical areas across the cortical hierarchy in five macaque monkeys. The laminar locations of recordings were histologically identified by electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Laminar recordings from additional species showed that the spectrolaminar pattern is highly preserved among primates-macaque, marmoset and human-but more dissimilar in mouse. Our results suggest the existence of a canonical layer-based and frequency-based mechanism for cortical computation.

Cacao Polyphenol-Rich Dark Chocolate Intake Contributes to Efficient Brain Activity during Cognitive Tasks: A Randomized, Single-Blinded, Crossover, and Dose-Comparison fMRI Study

Cacao polyphenol-enriched dark chocolate may have beneficial effects on human health, such as facilitating maintaining good performance in long-lasting cognitive tasks. This study examined the effects of dark chocolate intake on improving brain function during cognitive tasks using functional magnetic resonance imaging (fMRI). In this randomized, single-blinded, crossover, and dose-comparison study, 26 healthy middle-aged participants ingested dark chocolate (25 g) either with a low concentration (LC) (211.7 mg) or a high concentration (HC) (635 mg) of cacao polyphenols. Thereafter, their brain activities were analyzed during continuous and effortful cognitive tasks relevant to executive functioning using fMRI in two consecutive 15 min sessions (25 and 50 min after ingestion). We observed significant interaction effects between chocolate consumption and brain activity measurement sessions in the left dorsolateral prefrontal cortex and left inferior parietal lobule. After HC chocolate ingestion, these areas showed lower brain activity in the second session than in the first session; however, these areas showed higher activity in the second session after LC chocolate ingestion. These results suggest that cacao polyphenol-enriched dark chocolate enhances the efficient use of cognitive resources by reducing the effort of brain activity.

Architecture of the inferior parietal cortex in capuchin monkey

We studied the organization of the inferior parietal cortex (IPC) in five capuchin monkey (6 hemispheres) using cytoarchitectonic (Nissl), myeloarchitectonic (Gallyas), and immune-architectonic (SMI-32 monoclonal antibody) techniques. We partitioned the IPC into five distinct areas: PFG, PG, Opt, PFop, and PGop. Since we used parasagittal sections, we were not able to study area PF due to its far lateral position, which yielded slices that were tangential to the pial surface. Areas PFG, PG, and Opt were in the convexity close to the lateral sulcus, while PFop and PGop were positioned more posteriorly, in the opercular region of IPC. Of all the five regions, area Opt was the one most similar to its analogue in the macaque, especially as revealed with SMI-32 staining. Namely, in both primate species area Opt showed a low density of large pyramidal neurons. Additionally, the apical dendrites of these neurons were sparse and vertically orientated, resembling columns. We also found area PG to be similar: both species exhibited cell body layers with a radial arrangement. On the other hand, Nissl staining revealed area PFG to be architectonically different between New and Old-World monkeys: PFG in the capuchin showed a comparatively higher cell density than in macaques, especially in layers II and IV. These results suggest that evolution may have enabled the functional specialization of these brain regions based on behavioral demands of upper limb use. The small differences in the IPC of the two primates may be linked to interspecies variability.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

[Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning]

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Dynamic spatial coding in parietal cortex mediates tactile-motor transformation

Movements towards touch on the body require integrating tactile location and body posture information. Tactile processing and movement planning both rely on posterior parietal cortex (PPC) but their interplay is not understood. Here, human participants received tactile stimuli on their crossed and uncrossed feet, dissociating stimulus location relative to anatomy versus external space. Participants pointed to the touch or the equivalent location on the other foot, which dissociates sensory and motor locations. Multi-voxel pattern analysis of concurrently recorded fMRI signals revealed that tactile location was coded anatomically in anterior PPC but spatially in posterior PPC during sensory processing. After movement instructions were specified, PPC exclusively represented the movement goal in space, in regions associated with visuo-motor planning and with regional overlap for sensory, rule-related, and movement coding. Thus, PPC flexibly updates its spatial codes to accommodate rule-based transformation of sensory input to generate movement to environment and own body alike.

The structural connectivity mapping of the intralaminar thalamic nuclei

The intralaminar nuclei of the thalamus play a pivotal role in awareness, conscious experience, arousal, sleep, vigilance, as well as in cognitive, sensory, and sexual processing. Nonetheless, in humans, little is known about the direct involvement of these nuclei in such multifaceted functions and their structural connections in the brain. Thus, examining the versatility of structural connectivity of the intralaminar nuclei with the rest of the brain seems reasonable. Herein, we attempt to show the direct structural connectivity of the intralaminar nuclei to diencephalic, mesencephalic, and cortical areas using probabilistic tracking of the diffusion data from the human connectome project. The intralaminar nuclei fiber distributions span a wide range of subcortical and cortical areas. Moreover, the central medial and parafascicular nucleus reveal similar connectivity to the temporal, visual, and frontal cortices with only slight variability. The central lateral nucleus displays a refined projection to the superior colliculus and fornix. The centromedian nucleus seems to be an essential component of the subcortical somatosensory system, as it mainly displays connectivity via the medial and superior cerebellar peduncle to the brainstem and the cerebellar lobules. The subparafascicular nucleus projects to the somatosensory processing areas. It is interesting to note that all intralaminar nuclei have connections to the brainstem. In brief, the structural connectivity of the intralaminar nuclei aligns with the structural core of various functional demands for arousal, emotion, cognition, sensory, vision, and motor processing. This study sheds light on our understanding of the structural connectivity of the intralaminar nuclei with cortical and subcortical structures, which is of great interest to a broader audience in clinical and neuroscience research.

Brain Engagement During a Cognitive Flexibility Task Relates to Academic Performance in English Learners

English Learners (ELs), students from non-English-speaking backgrounds, are a fast-growing, understudied, group of students in the U.S. with unique learning challenges. Cognitive flexibility-the ability to switch between task demands with ease-may be an important factor in learning for ELs as they have to manage learning in their non-dominant language and access knowledge in multiple languages. We used functional MRI to measure cognitive flexibility brain activity in a group of Hispanic middle school ELs (N = 63) and related it to their academic skills. We found that brain engagement during the cognitive flexibility task was related to both out-of-scanner reading and math measures. These relationships were observed across the brain, including in cognitive control, attention, and default mode networks. This work suggests the real-world importance of cognitive flexibility for adolescent ELs, where individual differences in brain engagement were associated with educational outcomes.

Brain-wide network analysis of resting-state neuromagnetic data

The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.

Functional organization of social perception in the human brain

Humans rapidly extract diverse and complex information from ongoing social interactions, but the perceptual and neural organization of the different aspects of social perception remains unresolved. We showed short movie clips with rich social content to 97 healthy participants while their haemodynamic brain activity was measured with fMRI. The clips were annotated moment-to-moment for a large set of social features and 45 of the features were evaluated reliably between annotators. Cluster analysis of the social features revealed that 13 dimensions were sufficient for describing the social perceptual space. Three different analysis methods were used to map the social perceptual processes in the human brain. Regression analysis mapped regional neural response profiles for different social dimensions. Multivariate pattern analysis then established the spatial specificity of the responses and intersubject correlation analysis connected social perceptual processing with neural synchronization. The results revealed a gradient in the processing of social information in the brain. Posterior temporal and occipital regions were broadly tuned to most social dimensions and the classifier revealed that these responses showed spatial specificity for social dimensions; in contrast Heschl gyri and parietal areas were also broadly associated with different social signals, yet the spatial patterns of responses did not differentiate social dimensions. Frontal and subcortical regions responded only to a limited number of social dimensions and the spatial response patterns did not differentiate social dimension. Altogether these results highlight the distributed nature of social processing in the brain.

Myeloarchitectonic maps of the human cerebral cortex registered to surface and sections of a standard atlas brain

C. and O. Vogt had set up a research program with the aim of establishing a detailed cartography of the medullary fiber distribution of the human brain. As part of this program, around 200 cortical fields were differentiated based on their myeloarchitectural characteristics and mapped with regard to their exact location in the isocortex. The typical features were graphically documented and classified by a sophisticated linguistic coding. Their results have only recently received adequate attention and applications. The reasons for the revival of this spectrum of their research include interest in the myeloarchitecture of the cortex as a differentiating feature of the cortex architecture and function, as well as the importance for advanced imaging methodologies, particularly tractography and molecular imaging. Here, we describe our approach to exploit the original work of the Vogts and their co-workers to construct a myeloarchitectonic map that is referenced to the Atlas of the Human Brain (AHB) in standard space. We developed a semi-automatic pipeline for processing and integrating the various original maps into a single coherent map. To optimize the precision of the registration between the published maps and the AHB, we augmented the maps with topographic landmarks of the brains that were originally analyzed. Registration of all maps into the AHB opened several possibilities. First, for the majority of the fields, multiple maps from different authors are available, which allows for sophisticated statistical integration, for example, unification with a label-fusion technique. Second, each field in the myeloarchitectonic surface map can be visualized on the myelin-stained cross-section of the AHB at the best possible correspondence. The features of each field can be correlated with the fiber-stained cross-sections in the AHB and with the extensive published materials from the Vogt school and, if necessary, corrected. Third, mapping to the AHB allows the relationship between fiber characteristics of the cortex and the subcortex to be examined. Fourth, the cytoarchitectonic maps from Brodmann and von Economo and Koskinas, which are also registered to the AHB, can be compared. This option allows the study of the correspondence between cyto- and myeloarchitecture in each field. Finally, by using our "stripe" technology - where any other feature registered to the same space can be directly compared owing to the linear and parallel representation of the correlated cortex segments - this map becomes part of a multidimensional co-registration platform.

Statistical approaches to temporal and spatial autocorrelation in resting-state functional connectivity in mice measured with optical intrinsic signal imaging

Significance: Resting-state functional connectivity imaging in mice with optical intrinsic signal (OIS) imaging could provide a powerful translational tool for developing imaging biomarkers in preclinical disease models. However, statistical interpretation of correlation coefficients is hampered by autocorrelations in the data. Aim: We sought to better understand temporal and spatial autocorrelations in optical resting-state data. We then adapted statistical methods from functional magnetic resonance imaging to improve statistical inference. Approach: Resting-state data were obtained from mice using a custom-built OSI system. The autocorrelation time was calculated at each pixel, and z scores for correlation coefficients were calculated using Fisher transforms and variance derived from either Bartlett's method or xDF. The significance of each correlation coefficient was determined through control of the false discovery rate (FDR). Results: Autocorrelation was generally even across the cortex and parcellation reduced variance. Correcting variance with Bartlett's method resulted in a uniform reduction in z scores, with xDF preserving high z scores for highly correlated data. Control of the FDR resulted in reasonable thresholding of the correlation coefficient matrices. The use of Bartlett's method compared with xDF results in more conservative thresholding and fewer false positives under null hypothesis conditions. Conclusions: We developed streamlined methods for control of autocorrelation in OIS functional connectivity data in mice, and Bartlett's method is a reasonable compromise and simplification that allows for accurate autocorrelation correction. These results improve the rigor and reproducibility of functional neuroimaging in mice.

Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach

BACKGROUND

Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD.

METHODS

We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas.

RESULTS

The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients.

CONCLUSIONS

We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.

Processing of an Audiobook in the Human Brain Is Shaped by Cultural Family Background

Perception of the same narrative can vary between individuals depending on a listener’s previous experiences. We studied whether and how cultural family background may shape the processing of an audiobook in the human brain. During functional magnetic resonance imaging (fMRI), 48 healthy volunteers from two different cultural family backgrounds listened to an audiobook depicting the intercultural social life of young adults with the respective cultural backgrounds. Shared cultural family background increased inter-subject correlation of hemodynamic activity in the left-hemispheric Heschl’s gyrus, insula, superior temporal gyrus, lingual gyrus and middle temporal gyrus, in the right-hemispheric lateral occipital and posterior cingulate cortices as well as in the bilateral middle temporal gyrus, middle occipital gyrus and precuneus. Thus, cultural family background is reflected in multiple areas of speech processing in the brain and may also modulate visual imagery. After neuroimaging, the participants listened to the narrative again and, after each passage, produced a list of words that had been on their minds when they heard the audiobook during neuroimaging. Cultural family background was reflected as semantic differences in these word lists as quantified by a word2vec-generated semantic model. Our findings may depict enhanced mutual understanding between persons who share similar cultural family backgrounds.

ciftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R

There is significant interest in adopting surface- and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to perform surface smoothing to avoid issues associated with volumetric smoothing, improved inter-subject alignment, and reduced dimensionality. The CIFTI grayordinate file format introduced by the Human Connectome Project further advances grayordinate-based analysis by combining gray matter data from the left and right cortical hemispheres with gray matter data from the subcortex and cerebellum into a single file. Analyses performed in grayordinate space are well-suited to leverage information shared across the brain and across subjects through both traditional analysis techniques and more advanced statistical methods, including Bayesian methods. The R statistical environment facilitates use of advanced statistical techniques, yet little support for grayordinates analysis has been previously available in R Indeed, few comprehensive programmatic tools for working with CIFTI files have been available in any language Here, we present the ciftiTools R package, which provides a unified environment for reading, writing, visualizing and manipulating CIFTI files and related data formats. We illustrate ciftiTools’ convenient and user-friendly suite of tools for working with grayordinates and surface geometry data in R, and we describe how ciftiTools is being utilized to advance the statistical analysis of grayordinate-based functional MRI data.

Tonic somatosensory responses and deficits of tactile awareness converge in the parietal operculum

Although clinical neuroscience and the neuroscience of consciousness have long sought mechanistic explanations of tactile-awareness disorders, mechanistic insights are rare, mainly because of the difficulty of depicting the fine-grained neural dynamics underlying somatosensory processes.

Here, we combined the stereo-EEG responses to somatosensory stimulation with the lesion mapping of patients with a tactile-awareness disorder, namely tactile extinction.

Whereas stereo-EEG responses present different temporal patterns, including early/phasic and long-lasting/tonic activities, tactile-extinction lesion mapping co-localizes only with the latter. Overlaps are limited to the posterior part of the perisylvian regions, suggesting that tonic activities may play a role in sustaining tactile awareness. To assess this hypothesis further, we correlated the prevalence of tonic responses with the tactile-extinction lesion mapping, showing that they follow the same topographical gradient. Finally, in parallel with the notion that visuotactile stimulation improves detection in tactile-extinction patients, we demonstrated an enhancement of tonic responses to visuotactile stimuli, with a strong voxel-wise correlation with the lesion mapping.

The combination of these results establishes tonic responses in the parietal operculum as the ideal neural correlate of tactile awareness.

Brain rhythms define distinct interaction networks with differential dependence on anatomy

Cognitive functions are subserved by rhythmic neuronal synchronization across widely distributed brain areas. In 105 area pairs, we investigated functional connectivity (FC) through coherence, power correlation, and Granger causality (GC) in the theta, beta, high-beta, and gamma rhythms. Between rhythms, spatial FC patterns were largely independent. Thus, the rhythms defined distinct interaction networks. Importantly, networks of coherence and GC were not explained by the spatial distributions of the strengths of the rhythms. Those networks, particularly the GC networks, contained clear modules, with typically one dominant rhythm per module. To understand how this distinctiveness and modularity arises on a common anatomical backbone, we correlated, across 91 area pairs, the metrics of functional interaction with those of anatomical projection strength. Anatomy was primarily related to coherence and GC, with the largest effect sizes for GC. The correlation differed markedly between rhythms, being less pronounced for the beta and strongest for the gamma rhythm.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

miRNA-Based Signature Associated With Tumor Mutational Burden in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common malignant tumors. Tumor mutation burden (TMB) has become an independent biomarker for predicting the response to immune checkpoint inhibitors (ICIs). miRNAs play an important role in cancer-related immune regulation. However, the relationship between miRNA expression and TMB in COAD remains unclear. Therefore, the transcriptome profiling data, clinical data, mutation annotation data, and miRNA expression profiles for cases of COAD were downloaded from the TCGA database. Subsequently, 323 COAD cases were randomly divided into training and test sets. The differential expression of miRNAs in the high and low TMB groups in the training set was obtained as a signature using the least absolute shrinkage and selection operator (LASSO) logistic regression and verified in the test set. Based on the LASSO method, principal component analysis (PCA), and ROC, we found that the signature was credible because it can discriminate between high and low TMB levels. In addition, the correlation between the 18-miRNA-based signature and immune checkpoints was performed, followed by qRT-PCR, to measure the relative expression of 18 miRNAs in COAD patients. The miRNA-based model had a strong positive correlation with TMB and a weak positive correlation with CTLA4 and CD274 (PD-L1). However, no correlation was observed between the model and SNCA (PD-1). Finally, enrichment analysis of the 18 miRNAs was performed to explore their biological functions. The results demonstrated that 18 miRNAs were involved in the process of immunity and cancer pathways. In conclusion, the 18-miRNA-based signature can effectively predict and discriminate between the different TMB levels of COAD and provide a guide for its treatment with ICIs.

Predicting future cognitive decline with hyperbolic stochastic coding

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.

Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis

To compare neuroimaging data between subjects, images from individual sessions need to be aligned to a common reference or "atlas." Atlas registration of optical intrinsic signal imaging of mice, for example, is commonly performed using affine transforms with parameters determined by manual selection of canonical skull landmarks. Errors introduced by such procedures have not previously been investigated. We quantify the variability that arises from this process and consequent errors from misalignment that affect interpretation of functional neuroimaging data. We propose an improved method, using separately acquired high-resolution images and demonstrate improvements in variability and alignment using this method.

Error-signaling in the developing brain

While learning from mistakes is a lifelong process, the rate at which an individual makes errors on any given task decreases through late adolescence. Previous fMRI adult work indicates that several control brain networks are reliably active when participants make errors across multiple tasks. Less is known about the consistency and localization of error processing in the child brain because previous research has used single tasks. The current analysis pooled data across three studies to examine error-related task activation (two tasks per study, three tasks in total) for a group of 232 children aged 8-17 years. We found that, consistent with the adult literature, the majority of applied cingulo-opercular brain regions, including medial superior frontal cortex, dorsal anterior cingulate, and bilateral anterior insula, showed consistent error processing engagement in children across multiple tasks. Error-related activity in many of these cingulo-opercular regions correlated with task performance. However, unlike in the adult literature, we found a lack of error-related activation across tasks in dorsolateral frontal areas, and we also did not find any task-consistent relations with age in these regions. Our findings suggest that the task-general error processing signal in the developing brain is fairly robust and similar to adults, with the exception of lateral frontal cortex.

Pediatric ADHD symptom burden relates to distinct neural activity across executive function domains

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent childhood disorder marked by inattention and/or hyperactivity symptoms. ADHD may also relate to impaired executive function (EF), but is often studied in a single EF task per sample. The current study addresses the question of unique vs. overlapping relations in brain activity across multiple EF tasks and ADHD symptom burden. Three in-scanner tasks drawn from distinct EF domains (cognitive flexibility, working memory, and inhibition) were collected from children with and without an ADHD diagnosis (N = 63). Whole-brain activity and 11 regions of interest were correlated with parent reports of inattention and hyperactivity symptoms. Across the three EF domains, brain activity related to ADHD symptom burden, but the direction and location of these associations differed across tasks. Overall, activity in sensory and default mode network regions related to ADHD, and these relations did not consistently overlap across EF domains. We observed both distinct and overlapping patterns for inattention and hyperactivity symptoms. By studying multiple EF tasks in the same sample, we identified a heterogenous neural profile related to attention symptom burden in children. Our results inform ADHD characterization and treatment and explain some of the variable brain results related to EF and ADHD reported in the literature.

Action execution and action observation elicit mirror responses with the same temporal profile in human SII

The properties of the secondary somatosensory area (SII) have been described by many studies in monkeys and humans. Recent studies on monkeys, however, showed that beyond somatosensory stimuli, SII responds to a wider number of stimuli, a finding requiring a revision that human SII is purely sensorimotor. By recording cortical activity with stereotactic electroencephalography (stereo-EEG), we examined the properties of SI and SII in response to a motor task requiring reaching, grasping and manipulation, as well as the observation of the same actions. Furthermore, we functionally characterized this area with a set of clinical tests, including tactile, acoustical, and visual stimuli. The results showed that only SII activates both during execution and observation with a common temporal profile, whereas SI response were limited to execution. Together with their peculiar response to tactile stimuli, we conclude that the role of SII is pivotal also in the observation of actions involving haptic control.

Mapping the anatomy of perceptual pseudoneglect. A multivariate approach

Fundamental to the understanding of the functions of spatial cognition and attention is to clarify the underlying neural mechanisms. It is clear that relatively right-dominant activity in ventral and dorsal parieto-frontal cortex is associated with attentional reorienting, certain forms of mental imagery and spatial working memory for higher loads, while lesions mostly to right ventral areas cause spatial neglect with pathological attentional biases to the right side. In contrast, complementary leftward biases in healthy people, called pseudoneglect, have been associated with varying patterns of cortical activity. Notably, this inconsistency may be explained, at least in part, by the fact that pseudoneglect studies have often employed experimental paradigms that do not control sufficiently for cognitive processes unrelated to pseudoneglect. To address this issue, here we administered a carefully designed continuum of pseudoneglect and control tasks in healthy adults while using functional magnetic resonance imaging (fMRI). Data submitted to partial least square (PLS) imaging analysis yielded a significant latent variable that identified a right-dominant network of brain regions along the intra-occipital and -parietal sulci, frontal eye fields and right ventral cortex in association with perceptual pseudoneglect. Our results shed new light on the interplay of attentional and cognitive systems in pseudoneglect.

Functional boundaries in the human cerebellum revealed by a multi-domain task battery

There is compelling evidence that the human cerebellum is engaged in a wide array of motor and cognitive tasks. A fundamental question centers on whether the cerebellum is organized into distinct functional subregions. To address this question, we employed a rich task battery designed to tap into a broad range of cognitive processes. During four functional MRI sessions, participants performed a battery of 26 diverse tasks comprising 47 unique conditions. Using the data from this multi-domain task battery, we derived a comprehensive functional parcellation of the cerebellar cortex and evaluated it by predicting functional boundaries in a novel set of tasks. The new parcellation successfully identified distinct functional subregions, providing significant improvements over existing parcellations derived from task-free data. Lobular boundaries, commonly used to summarize functional data, did not coincide with functional subdivisions. The new parcellation provides a functional atlas to guide future neuroimaging studies.

Ciftify: A framework for surface-based analysis of legacy MR acquisitions

The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.

Brain activity in struggling readers before intervention relates to future reading gains

Neural markers for reading-related changes in response to intervention could inform intervention plans by serving as a potential index of the malleability of the reading network in struggling readers. Of particular interest is the role of brain activation outside the reading network, especially in executive control networks important for reading comprehension. However, it is unclear whether any intervention-related executive control changes in the brain are specific to reading tasks or reflect more domain general changes. Brain changes associated with reading gains over time were compared for a sentence comprehension task as well as for a non-lexical executive control task (a behavioral inhibition task) in upper-elementary struggling readers, and in grade-matched non-struggling readers. Functional MRI scans were conducted before and after 16 weeks of reading intervention. Participants were grouped as improvers and non-improvers based on the consistency and size of post-intervention gains across multiple post-test measures. Engagement of the right fusiform during the reading task, both before and after intervention, was related to gains from remediation. Additionally, pre-intervention activation in regions that are part of the default-mode network (precuneus) and the fronto-parietal network (right posterior middle temporal gyrus) separated improvers and non-improvers from non-struggling readers. None of these differences were observed during the non-lexical inhibitory control task, indicating that the brain changes seen related to intervention outcome in struggling readers were specific to the reading process.

Rapid and specific processing of person-related information in human anterior temporal lobe

The anterior temporal lobe (ATL), located at the tip of the human temporal lobes, has been heavily implicated in semantic processing by neuropsychological and functional imaging studies. These techniques have revealed a hemispheric specialization of ATL, but little about the time scale on which it operates. Here we show that ATL is specifically activated in intracerebral recordings when subjects discriminate the gender of an actor presented in a static frame followed by a video. ATL recording sites respond briefly (100 ms duration) to the visual static presentation of an actor in a task-, but not in a stimulus-duration-dependent way. Their response latencies correlate with subjects' reaction times, as do their activity levels, but oppositely in the two hemispheres operating in a push-pull fashion. Comparison of ATL time courses with those of more posterior, less specific regions emphasizes the role of inhibitory operations sculpting the fast ATL responses underlying semantic processing.

Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann's area 32 and area 21

Optical coherence tomography is an optical technique that uses backscattered light to highlight intrinsic structure, and when applied to brain tissue, it can resolve cortical layers and fiber bundles. Optical coherence microscopy (OCM) is higher resolution (i.e., 1.25 µm) and is capable of detecting neurons. In a previous report, we compared the correspondence of OCM acquired imaging of neurons with traditional Nissl stained histology in entorhinal cortex layer II. In the current method-oriented study, we aimed to determine the colocalization success rate between OCM and Nissl in other brain cortical areas with different laminar arrangements and cell packing density. We focused on two additional cortical areas: medial prefrontal, pre-genual Brodmann area (BA) 32 and lateral temporal BA 21. We present the data as colocalization matrices and as quantitative percentages. The overall average colocalization in OCM compared to Nissl was 67% for BA 32 (47% for Nissl colocalization) and 60% for BA 21 (52% for Nissl colocalization), but with a large variability across cases and layers. One source of variability and confounds could be ascribed to an obscuring effect from large and dense intracortical fiber bundles. Other technical challenges, including obstacles inherent to human brain tissue, are discussed. Despite limitations, OCM is a promising semi-high throughput tool for demonstrating detail at the neuronal level, and, with further development, has distinct potential for the automatic acquisition of large databases as are required for the human brain.

A numerical simulation of neural fields on curved geometries

Despite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of ne;urons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional neural field equation posed on a periodic square domain, the curved surface of a torus, and the cortical surface of a rat brain, the latter of which is constructed using neuroimaging data. Our results are twofold: Firstly, we find that collocation techniques are able to replicate solutions obtained using more standard Fourier based methods on a flat, periodic domain, independent of the underlying mesh. This result is particularly significant given the highly irregular nature of the type of meshes derived from modern neuroimaging data. And secondly, by deploying efficient numerical schemes to compute geodesics, our approach is not only capable of modelling macroscopic pattern formation on realistic cortical geometries, but can also be extended to include cortical architectures of more physiological relevance. Importantly, such an approach provides a means by which to investigate the influence of cortical geometry upon the nucleation and propagation of spatially localised neural activity and beyond. It thus promises to provide model-based insights into disorders like epilepsy, or spreading depression, as well as healthy cognitive processes like working memory or attention.

Gamma Synchronization between V1 and V4 Improves Behavioral Performance

Behavior is often driven by visual stimuli, relying on feedforward communication from lower to higher visual areas. Effective communication depends on enhanced interareal coherence, but it remains unclear whether this coherence occurs at an optimal phase relation that actually improves stimulus transmission to behavioral report. We recorded local field potentials from V1 and V4 of macaques performing an attention task during which they reported changes in the attended stimulus. V1-V4 gamma synchronization immediately preceding the stimulus change partly predicted subsequent reaction times (RTs). RTs slowed systematically as trial-by-trial interareal gamma phase relations deviated from the phase relation at which V1 and V4 synchronized on average. V1-V4 gamma phase relations accounted for RT differences of 13-31 ms. Effects were specific to the attended stimulus and not explained by local power or phase. Thus, interareal gamma synchronization occurs at the optimal phase relation for transmission of sensory inputs to motor responses.

An Open Resource for Non-human Primate Imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

Functional reorganisation and recovery following cortical lesions: A preliminary study in macaque monkeys

Damage following traumatic brain injury or stroke can often extend beyond the boundaries of the initial insult and can lead to maladaptive cortical reorganisation. On the other hand, beneficial cortical reorganisation leading to recovery of function can also occur. We used resting state FMRI to investigate how cortical networks in the macaque brain change across time in response to lesions to the prefrontal cortex, and how this reorganisation correlated with changes in behavioural performance in cognitive tasks. After prelesion testing and scanning, two monkeys received a lesion to regions surrounding the left principal sulcus followed by periodic testing and scanning. Later, the animals received another lesion to the opposite hemisphere and additional testing and scanning. Following the first lesion, we observed both a behavioural impairment and decrease in functional connectivity, predominantly in frontal-frontal networks. Approximately 8 weeks later, performance and connectivity patterns both improved. Following the second lesion, we observed a further behavioural deficit and decrease in connectivity that showed little recovery. We discuss how different mechanisms including alternate behavioural strategies and reorganisation of specific prefrontal networks may have led to improvements in behaviour. Further work will be needed to confirm these mechanisms.

Interactions between neural decision-making circuits predict long-term dietary treatment success in obesity

Although dietary decision-making is regulated by multiple interacting neural controllers, their impact on dietary treatment success in obesity has only been investigated individually. Here, we used fMRI to test how well interactions between the Pavlovian system (automatically triggering urges of consumption after food cue exposure) and the goal-directed system (considering long-term consequences of food decisions) predict future dietary success achieved in 39 months. Activity of the Pavlovian system was measured with a cue-reactivity task by comparing perception of food versus control pictures, activity of the goal-directed system with a food-specific delay discounting paradigm. Both tasks were applied in 30 individuals with obesity up to five times: Before a 12-week diet, immediately thereafter, and at three annual follow-up visits. Brain activity was analyzed in two steps. In the first, we searched for areas involved in Pavlovian processes and goal-directed control across the 39-month study period with voxel-wise linear mixed-effects (LME) analyses. In the second, we computed network parameters reflecting the covariation of longitudinal voxel activity (i.e. principal components) in the regions identified in the first step and used them to predict body mass changes across the 39 months with LME models. Network analyses testing the link of dietary success with activity of the individual systems as reference found a moderate negative link to Pavlovian activity primarily in left hippocampus and a moderate positive association to goal-directed activity primarily in right inferior parietal gyrus. A cross-paradigm network analysis that integrated activity measured in both tasks revealed a strong positive link for interactions between visual Pavlovian areas and goal-directed decision-making regions mainly located in right insular cortex. We conclude that adaptation of food cue processing resources to goal-directed control activity is an important prerequisite of sustained dietary weight loss, presumably since the latter activity can modulate Pavlovian urges triggered by frequent cue exposure in everyday life.

Parcellating Cerebral Cortex: How Invasive Animal Studies Inform Noninvasive Mapmaking in Humans

The cerebral cortex in mammals contains a mosaic of cortical areas that differ in function, architecture, connectivity, and/or topographic organization. A combination of local connectivity (within-area microcircuitry) and long-distance (between-area) connectivity enables each area to perform a unique set of computations. Some areas also have characteristic within-area mesoscale organization, reflecting specialized representations of distinct types of information. Cortical areas interact with one another to form functional networks that mediate behavior, and each area may be a part of multiple, partially overlapping networks. Given their importance to the understanding of brain organization, mapping cortical areas across species is a major objective of systems neuroscience and has been a century-long challenge. Here, we review recent progress in multi-modal mapping of mouse and nonhuman primate cortex, mainly using invasive experimental methods. These studies also provide a neuroanatomical foundation for mapping human cerebral cortex using noninvasive neuroimaging, including a new map of human cortical areas that we generated using a semiautomated analysis of high-quality, multimodal neuroimaging data. We contrast our semiautomated approach to human multimodal cortical mapping with various extant fully automated human brain parcellations that are based on only a single imaging modality and offer suggestions on how to best advance the noninvasive brain parcellation field. We discuss the limitations as well as the strengths of current noninvasive methods of mapping brain function, architecture, connectivity, and topography and of current approaches to mapping the brain's functional networks.

The impact of traditional neuroimaging methods on the spatial localization of cortical areas

Localizing human brain functions is a long-standing goal in systems neuroscience. Toward this goal, neuroimaging studies have traditionally used volume-based smoothing, registered data to volume-based standard spaces, and reported results relative to volume-based parcellations. A novel 360-area surface-based cortical parcellation was recently generated using multimodal data from the Human Connectome Project, and a volume-based version of this parcellation has frequently been requested for use with traditional volume-based analyses. However, given the major methodological differences between traditional volumetric and Human Connectome Project-style processing, the utility and interpretability of such an altered parcellation must first be established. By starting from automatically generated individual-subject parcellations and processing them with different methodological approaches, we show that traditional processing steps, especially volume-based smoothing and registration, substantially degrade cortical area localization compared with surface-based approaches. We also show that surface-based registration using features closely tied to cortical areas, rather than to folding patterns alone, improves the alignment of areas, and that the benefits of high-resolution acquisitions are largely unexploited by traditional volume-based methods. Quantitatively, we show that the most common version of the traditional approach has spatial localization that is only 35% as good as the best surface-based method as assessed using two objective measures (peak areal probabilities and "captured area fraction" for maximum probability maps). Finally, we demonstrate that substantial challenges exist when attempting to accurately represent volume-based group analysis results on the surface, which has important implications for the interpretability of studies, both past and future, that use these volume-based methods.

Correlated Gene Expression and Anatomical Communication Support Synchronized Brain Activity in the Mouse Functional Connectome

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting-state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI.SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still primarily unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting-state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be used to streamline preclinical animal studies of disease.

Multiple time courses of somatosensory responses in human cortex

Here we show how anatomical and functional data recorded from patients undergoing stereo-EEG can be used to decompose the cortical processing following nerve stimulation in different stages characterized by specific topography and time course. Tibial, median and trigeminal nerves were stimulated in 96 patients, and the increase in gamma power was evaluated over 11878 cortical sites. All three nerve datasets exhibited similar clusters of time courses: phasic, delayed/prolonged and tonic, which differed in topography, temporal organization and degree of spatial overlap. Strong phasic responses of the three nerves followed the classical somatotopic organization of SI, with no overlap in either time or space. Delayed responses presented overlaps between pairs of body parts in both time and space, and were confined to the dorsal motor cortices. Finally, tonic responses occurred in the perisylvian region including posterior insular cortex and were evoked by the stimulation of all three nerves, lacking any spatial and temporal specificity. These data indicate that the somatosensory processing following nerve stimulation is a multi-stage hierarchical process common to all three nerves, with the different stages likely subserving different functions. While phasic responses represent the neural basis of tactile perception, multi-nerve tonic responses may represent the neural signature of processes sustaining the capacity to become aware of tactile stimuli.

Prediction of Alzheimer's Disease in Amnestic Mild Cognitive Impairment Subtypes: Stratification Based on Imaging Biomarkers

BACKGROUND

Prediction of progression to Alzheimer's disease (AD) in amnestic mild cognitive impairment (MCI) is challenging because of its heterogeneity.

OBJECTIVE

To evaluate a stratification method on different cohorts and to investigate whether stratification in amnestic MCI could improve prediction accuracy.

METHODS

We identified 80 and 79 patients with amnestic MCI from different cohorts, respectively. They underwent baseline magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. We performed hierarchical clustering with three imaging biomarkers: Brain volume on MRI, left hippocampus grey matter loss on MRI, and left inferior temporal gyrus glucose hypometabolism on FDG-PET. Regions-of-interest for biomarkers were defined by the Automated Anatomical Labeling atlas. We performed voxel-wise statistical parametric mapping to explore differences between clusters in patterns of grey matter loss and glucose hypometabolism. We compared time to progression using an interval-censored parametric model. We evaluated predictive performance using logistic regression.

RESULTS

Similar clusters were found in different cohorts. MCI1 had the healthiest biomarker profile of cognitive performance and imaging biomarkers. MCI2 had cognitive performance and MRI measures intermediate between those of nonconverters and converters. MCI3 showed the severest reduction in brain volume and left hippocampal atrophy. MCI4 showed remarkable glucose hypometabolism in the left inferior temporal gyrus, and also demonstrated significant decreases in most cognitive scores, including non-memory functions. MCI4 showed the highest risk for progression. The prediction of progression of MCI2 especially benefited from the stratification.

CONCLUSION

Stratification with imaging biomarkers in amnestic MCI can be a good approach for improving predictive performance.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.

The Detection of Phase Amplitude Coupling during Sensory Processing

There is increasing interest in understanding how the phase and amplitude of distinct neural oscillations might interact to support dynamic communication within the brain. In particular, previous work has demonstrated a coupling between the phase of low frequency oscillations and the amplitude (or power) of high frequency oscillations during certain tasks, termed phase amplitude coupling (PAC). For instance, during visual processing in humans, PAC has been reliably observed between ongoing alpha (8–13 Hz) and gamma-band (>40 Hz) activity. However, the application of PAC metrics to electrophysiological data can be challenging due to numerous methodological issues and lack of coherent approaches within the field. Therefore, in this article we outline the various analysis steps involved in detecting PAC, using an openly available MEG dataset from 16 participants performing an interactive visual task. Firstly, we localized gamma and alpha-band power using the Fieldtrip toolbox, and extracted time courses from area V1, defined using a multimodal parcelation scheme. These V1 responses were analyzed for changes in alpha-gamma PAC, using four common algorithms. Results showed an increase in alpha (7–13 Hz)–gamma (40–100 Hz) PAC in response to the visual grating stimulus, though specific patterns of coupling were somewhat dependent upon the algorithm employed. Additionally, post-hoc analyses showed that these results were not driven by the presence of non-sinusoidal oscillations, and that trial length was sufficient to obtain reliable PAC estimates. Finally, throughout the article, methodological issues and practical guidelines for ongoing PAC research will be discussed.