scholarly journals A data-driven approach to the automated mapping of functional brain topographies across species

2018 ◽  
Author(s):  
Wolfgang M. Pauli
Keyword(s):  
Animal Models ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Brain Regions ◽  
Behavioral Neuroscience ◽  
Open Questions ◽  
Dorsolateral Prefrontal ◽  
Neuroimaging Data ◽  
Data Driven Approach ◽  
Species Mapping

AbstractBehavioral neuroscience has made great strides in developing animal models of human behavior and psychiatric disorders. Animal models allow for the formulation of hypotheses regarding the mechanisms underlying psychiatric disorders, and the opportunity to test these hypotheses using procedures that are too invasive for human participants. However, recent scientific reviews have highlighted the low success rate of translating results from animal models into clinical interventions in humans. A potential roadblock is that bidirectional functional mappings between the human and rodent brain are incomplete. To narrow this gap, we created a framework, Neurobabel, for performing large-scale automated synthesis of human neuroimaging data and behavioral neuroscience data. By leveraging the semantics of how researchers within each field describe their studies, this framework enables region to region mapping of brain regions across species, as well as cross-species mapping of psychological functions. As a proof of concept, we utilize the framework to create a functional cross-species mapping between the amygdala and hippocampus for fear-related and spatial memories, respectively. We then proceed to address two open questions in the field: (1) Do rodents have a dorsolateral prefrontal cortex? (2) Which human brain region corresponds to the rodent prelimbic cortex?

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

2020 ◽  
Author(s):  
Kristen R. Maynard ◽  
Leonardo Collado-Torres ◽  
Lukas M. Weber ◽  
Cedric Uytingco ◽  
Brianna K. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Web Application ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Sequencing Data ◽  
Dorsolateral Prefrontal ◽  
Transcriptomics Data

AbstractWe used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

scholarly journals Regional heterogeneity in gene expression, regulation and coherence in hippocampus and dorsolateral prefrontal cortex across development and in schizophrenia

2018 ◽  
Author(s):  
L Collado-Torres ◽  
EE Burke ◽  
A Peterson ◽  
JH Shin ◽  
RE Straub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Brain Region ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Second Phase ◽  
Regional Heterogeneity ◽  
Surrogate Variable Analysis ◽  
Dorsolateral Prefrontal

AbstractRecent large-scale genomics efforts have better characterized the molecular correlates of schizophrenia in postmortem human neocortex, but not hippocampus which is a brain region prominently implicated in its pathogenesis. Here in the second phase of the BrainSeq Consortium (Phase II), we have generated RiboZero RNA-seq data for 900 samples across both the dorsolateral prefrontal cortex (DLPFC) and the hippocampus (HIPPO) for 551 individuals (286 affected by schizophrenia disorder: SCZD). We identify substantial regional differences in gene expression, in both pre- and post-natal life, and find widespread differences in how genes are regulated across development. By extending quality surrogate variable analysis (qSVA) to multiple brain regions, we identified 48 and 245 differentially expressed genes (DEG) by SCZD diagnosis (FDR<5%) in HIPPO and DLPFC, respectively, with surprisingly minimal overlap in DEG between the two brain regions. We further identified 205,618 brain region-dependent eQTLs (FDR<1%) and found that 124 GWAS risk loci contain eQTLs in at least one of the regions. We also identify potential molecular correlates of in vivo evidence of altered prefrontal-hippocampal functional coherence in schizophrenia. These results underscore the complexity and regional heterogeneity of the transcriptional correlates of schizophrenia, and suggest future schizophrenia therapeutics may need to target molecular pathologies localized to specific brain regions.

scholarly journals Neuroimaging studies of autobiographical event memory

2001 ◽  
Vol 356 (1413) ◽  
pp. 1441-1451 ◽  
Author(s):  
Eleanor A. Maguire
Keyword(s):  
Dorsolateral Prefrontal Cortex ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Event Memory ◽  
General Terms ◽  
Left Hippocampus ◽  
Dorsolateral Prefrontal ◽  
Neuroimaging Data ◽  
Autobiographical Event

Commonalities and differences in findings across neuroimaging studies of autobiographical event memory are reviewed. In general terms, the overall pattern across studies is of medial and left–lateralized activations associated with retrieval of autobiographical event memories. It seems that the medial frontal cortex and left hippocampus in particular are responsive to such memories. However, there are also inconsistencies across studies, for example in the activation of the hippocampus and dorsolateral prefrontal cortex. It is likely that methodological differences between studies contribute to the disparate findings. Quantifying and assessing autobiographical event memories presents a challenge in many domains, including neuroimaging. Methodological factors that may be pertinent to the interpretation of the neuroimaging data and the design of future experiments are discussed. Consideration is also given to aspects of memory that functional neuroimaging might be uniquely disposed to examine. These include assessing the functionality of damaged tissue in patients and the estimation of inter–regional communication (effective connectivity) between relevant brain regions.

scholarly journals Regional specialization within the human striatum for diverse psychological functions

2016 ◽  
Vol 113 (7) ◽  
pp. 1907-1912 ◽  
Author(s):  
Wolfgang M. Pauli ◽  
Randall C. O’Reilly ◽  
Tal Yarkoni ◽  
Tor D. Wager
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Reward Processing ◽  
Cortical Activation ◽  
Psychological Processes ◽  
Resting State Functional Connectivity ◽  
Regional Specialization ◽  
Data Driven Approach ◽  
Psychological Functions ◽  
Cortical Regions

Decades of animal and human neuroimaging research have identified distinct, but overlapping, striatal zones, which are interconnected with separable corticostriatal circuits, and are crucial for the organization of functional systems. Despite continuous efforts to subdivide the human striatum based on anatomical and resting-state functional connectivity, characterizing the different psychological processes related to each zone remains a work in progress. Using an unbiased, data-driven approach, we analyzed large-scale coactivation data from 5,809 human imaging studies. We (i) identified five distinct striatal zones that exhibited discrete patterns of coactivation with cortical brain regions across distinct psychological processes and (ii) identified the different psychological processes associated with each zone. We found that the reported pattern of cortical activation reliably predicted which striatal zone was most strongly activated. Critically, activation in each functional zone could be associated with distinct psychological processes directly, rather than inferred indirectly from psychological functions attributed to associated cortices. Consistent with well-established findings, we found an association of the ventral striatum (VS) with reward processing. Confirming less well-established findings, the VS and adjacent anterior caudate were associated with evaluating the value of rewards and actions, respectively. Furthermore, our results confirmed a sometimes overlooked specialization of the posterior caudate nucleus for executive functions, often considered the exclusive domain of frontoparietal cortical circuits. Our findings provide a precise functional map of regional specialization within the human striatum, both in terms of the differential cortical regions and psychological functions associated with each striatal zone.

scholarly journals Brainstem modulation of large-scale intrinsic cortical activity correlations

2019 ◽  
Author(s):  
Ruud L. van den Brink ◽  
Thomas Pfeffer ◽  
Tobias Donner
Keyword(s):  
Psychiatric Disorders ◽  
Large Scale ◽  
Brain Activity ◽  
Cortical Activity ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Non Invasive ◽  
Correlated Activity ◽  
Intrinsic Fluctuations ◽  
Specific Receptors

Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson’s disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distill a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically-inspired biomarkers and individualized treatments of these disorders.

scholarly journals Cerebellum and Emotion in Morality

2021 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Moral Functioning ◽  
Neuroimaging Data ◽  
Crus I ◽  
The Right ◽  
Brain Image Analysis ◽  
The Relationship

In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but less-studies topic with NeuroSynth, a tool for large-scale brain image analysis, while addressing issues associated with reverse inference. The result from analysis demonstrated that brain regions in the cerebellum, the right Crus I and Crus II in particular, were specifically associated with morality in general. I discussed the potential implications of the finding based on clinical and functional neuroimaging studies of the cerebellum, emotional functioning, and neural networks for diverse psychological processes.

scholarly journals Revealing the dual streams of speech processing

2016 ◽  
Vol 113 (52) ◽  
pp. 15108-15113 ◽  
Author(s):  
Julius Fridriksson ◽  
Grigori Yourganov ◽  
Leonardo Bonilha ◽  
Alexandra Basilakos ◽  
Dirk-Bart Den Ouden ◽  
...  
Keyword(s):  
Speech Processing ◽  
Large Scale ◽  
A Priori ◽  
Dorsal Stream ◽  
Brain Regions ◽  
Ventral Stream ◽  
Complete Agreement ◽  
Human Speech ◽  
Lexical Semantic ◽  
Data Driven Approach

Several dual route models of human speech processing have been proposed suggesting a large-scale anatomical division between cortical regions that support motor–phonological aspects vs. lexical–semantic aspects of speech processing. However, to date, there is no complete agreement on what areas subserve each route or the nature of interactions across these routes that enables human speech processing. Relying on an extensive behavioral and neuroimaging assessment of a large sample of stroke survivors, we used a data-driven approach using principal components analysis of lesion-symptom mapping to identify brain regions crucial for performance on clusters of behavioral tasks without a priori separation into task types. Distinct anatomical boundaries were revealed between a dorsal frontoparietal stream and a ventral temporal–frontal stream associated with separate components. Collapsing over the tasks primarily supported by these streams, we characterize the dorsal stream as a form-to-articulation pathway and the ventral stream as a form-to-meaning pathway. This characterization of the division in the data reflects both the overlap between tasks supported by the two streams as well as the observation that there is a bias for phonological production tasks supported by the dorsal stream and lexical–semantic comprehension tasks supported by the ventral stream. As such, our findings show a division between two processing routes that underlie human speech processing and provide an empirical foundation for studying potential computational differences that distinguish between the two routes.

Neuroimaging and modeling

2014 ◽  
Vol 11 (04) ◽  
pp. 245-253
Author(s):  
S. B. Eickhoff ◽  
D. Bzdok
Keyword(s):  
Psychiatric Disorders ◽  
Large Scale ◽  
Effective Connectivity ◽  
Network Models ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Causal Modeling ◽  
Functional Coupling ◽  
Response Patterns ◽  
Functional Relationships

SummaryThe links between symptomatic phenomenology of psychiatric disorders and their neurobiological pathophysiology are still understood only in fragments. While functional and structural neuroimaging methods have leveraged our knowledge of regional dysfunction in psychiatric disorders, emerging novel approaches more focused on brain networks or neural patterns promise additional forward progress. Activation likelihood estimation (ALE) performs quantitative large-scale aggregation of neuroimaging findings. Resting-state (RS) correlation captures networks of functional relationships between regions in the idling, non-goal-focused brain. In contrast, meta-analytic connectivity modeling (MACM) captures functional coupling between brain regions in the context of experimental paradigms. These methods may furthermore be exploited to provide data-driven parcellations (CBP, connectivity-based parcellation) of larger brain regions into distinct functional modules. Dynamic causal modeling (DCM), in turn, allows for the automatic selection among a set of connectional network models to delineate effective connectivity dynamics during experimental paradigms. Finally, machine learning (ML) allows for the automatic detection and prediction of diagnosis/treatment-response patterns in massive datasets. Capitalizing on this toolbox of computational modeling methods might considerably further psychiatry and thus benefit patients with mental disorders.

Neurocognitive and Perceptual Processing in Genetic Mouse Models of Schizophrenia: Emerging Lessons

2019 ◽  
Vol 25 (6) ◽  
pp. 597-619
Author(s):  
Anastasia Diamantopoulou ◽  
Joseph A. Gogos
Keyword(s):  
Animal Models ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Perceptual Processing ◽  
Great Promise ◽  
Biological Targets ◽  
The Past ◽  
Disease Mechanisms ◽  
Consistent Approach ◽  
Genetic Mouse Models

During the past two decades, the number of animal models of psychiatric disorders has grown exponentially. Of these, genetic animal models that are modeled after rare but highly penetrant mutations hold great promise for deciphering critical molecular, synaptic, and neurocircuitry deficits of major psychiatric disorders, such as schizophrenia. Animal models should aim to focus on core aspects rather than capture the entire human disease. In this context, animal models with strong etiological validity, where behavioral and neurophysiological phenotypes and the features of the disease being modeled are in unambiguous homology, are being used to dissect both elementary and complex cognitive and perceptual processing deficits present in psychiatric disorders at the level of neurocircuitry, shedding new light on critical disease mechanisms. Recent progress in neuroscience along with large-scale initiatives that propose a consistent approach in characterizing these deficits across different laboratories will further enhance the efficacy of these studies that will ultimately lead to identifying new biological targets for drug development.

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