scholarly journals Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma

Brain ◽  
2020 ◽  
Vol 143 (11) ◽  
pp. 3294-3307
Author(s):  
Ayan S Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G Hart ◽  
John Suckling
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain Atlas ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph

Abstract For decades, it has been known that gliomas follow a non-random spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the localization patterns of gliomas could provide clues to the origins of these types of tumours, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumour frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells of origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signalling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

scholarly journals NIMG-13. GENETIC, CELLULAR, AND CONNECTOMIC CHARACTERIZATION OF THE ADULT HUMAN BRAIN REGIONS COMMONLY PLAGUED BY GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii149-ii149
Author(s):  
Ayan Mandal ◽  
Rafael Romero-Garcia ◽  
Michael Hart ◽  
John Suckling
Keyword(s):  
Human Brain ◽  
Occipital Lobe ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Brain Atlas ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Multiple Graph

Abstract For decades, it has been known that gliomas follow a nonrandom spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the glioma localization patterns could lend clues to the origins of these types of tumors, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrate that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells-of-origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signaling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

scholarly journals Genetic, Cellular, and Connectomic Characterization of the Adult Human Brain Regions Commonly Plagued by Glioma

2020 ◽  
Author(s):  
Ayan S. Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G. Hart ◽  
John Suckling
Keyword(s):  
Brain Regions ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph ◽  
Anatomic Localization ◽  
The Brain

AbstractA better understanding of the nonrandom localization patterns of gliomas across the brain could lend clues to the origins of these types of tumors. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with stem-like cells also exhibited a high glioma frequency. Furthermore, gliomas were localized to brain regions enriched with the expression of genes associated with chromatin organization and synaptic signaling. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of oncogenesis.

scholarly journals Central hormone-sensitive lipase is located at synapses and is necessary for normal memory performance in mice

2021 ◽  
Author(s):  
Cecilia Skoug ◽  
Cecilia Holm ◽  
João M.N. Duarte
Keyword(s):  
Neuronal Activity ◽  
Memory Performance ◽  
Brain Dysfunction ◽  
Hormone Sensitive Lipase ◽  
Long Term Memory ◽  
Low Grade ◽  
Bioactive Lipids ◽  
Expression Of Genes ◽  
Hormone Sensitive ◽  
Behavioural Tests

AbstractHormone-sensitive lipase (HSL) is mainly present in the adipose tissue where it hydrolyses diacylglycerol. Although brain expression of HSL has been reported, its presence in different cellular compartments is uncertain, and its role in regulating brain lipid metabolism remains hitherto unexplored. We propose that HSL has a role in regulating the availability of bioactive lipids necessary for adequate neuronal function. Therefore, we tested the hypothesis that dampening HSL activity leads to brain dysfunction. We found HSL protein and activity throughout all the mouse brain, localised in neurons and especially enriched in synapses. HSL null mice were then analysed using a battery of behavioural tests. Relative to wild-type littermates, HSL null mice showed impaired short- and long-term memory, but preserved exploratory behaviours. Molecular analysis of the cortex and hippocampus showed increased expression of genes involved in glucose utilization in the hippocampus but not cortex of HSL null mice compared to controls. Lipidomics analyses indicated an impact of HSL deletion on the profile of bioactive lipids, including endocannabinoids and eicosanoids that are known to modulate neuronal activity, cerebral blood blow and inflammation processes. Accordingly, mild increases in expression of pro-inflammatory cytokines suggest low grade inflammation in HSL null mice compared to littermates. We conclude that HSL has a homeostatic role in maintaining pools of lipids that are needed for brain function. It remains to be tested, however, whether the recruitment of HSL for the synthesis of these lipids occurs during increased neuronal activity, or whether HSL participates in neuroinflammatory responses.

scholarly journals The functional and genetic associations of neuroimaging data: a toolbox

2017 ◽  
Author(s):  
Zhaowen Liu ◽  
Edmund T. Rolls ◽  
Jie Zhang ◽  
Ming Yang ◽  
Jingnan Du ◽  
...  
Keyword(s):  
Permutation Test ◽  
Statistical Tests ◽  
Brain Regions ◽  
Brain Atlas ◽  
Genetic Associations ◽  
Generate Functional ◽  
Exact Test ◽  
Human Connectome Project ◽  
Neuroimaging Data ◽  
Highly Correlated

AbstractAdvances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and to identify the roots of psychiatric diseases. However, the results generated by most neuroimaging studies, i.e., activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. We describe a Brain Annotation Toolbox (BAT), a toolbox that helps to generate functional and genetic annotations for neuroimaging results. The toolbox can take data from brain regions identified with an atlas, or from brain regions identified as activated in tasks, or from functional connectivity links or networks of links. Then, the voxel-level functional description from the Neurosynth database and the gene expression profile from the Allen Brain Atlas are used to generate functional and genetic knowledge for such region-level data. Parametric (Fisher’s exact test) or non-parametric (permutation test) statistical tests are adopted to identify significantly related functional descriptors and genes for the neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and gene co-expression networks are highly correlated for many major brain atlases. One application of BAT is to help provide functional and genetic annotations for the newly discovered regions with unknown functions, e.g., the 97 new regions identified in the Human Connectome Project. Importantly too, this toolbox can help understand differences between patients with psychiatric disorders and controls, and this is demonstrated using data for schizophrenia and autism, for which the functional and genetic annotations for the neuroimaging data differences between patients and controls are consistent with each other and help with the interpretation of the differences.

scholarly journals Fear-induced brain activations distinguish anxious and trauma-exposed brains

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenfu Wen ◽  
Marie-France Marin ◽  
Jennifer Urbano Blackford ◽  
Zhe Sage Chen ◽  
Mohammed R. Milad
Keyword(s):  
Neural Network ◽  
Fear Conditioning ◽  
Psychiatric Diagnosis ◽  
Data Analytics ◽  
Conditioned Fear ◽  
Brain Regions ◽  
Brain Network ◽  
Neuroimaging Data ◽  
Task Irrelevant ◽  
Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

Neural Mechanisms of Negative Symptoms

1989 ◽  
Vol 155 (S7) ◽  
pp. 93-98 ◽  
Author(s):  
Nancy C. Andreasen
Keyword(s):  
Frontal Cortex ◽  
Negative Symptoms ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Brain Dysfunction ◽  
Neural Mechanisms ◽  
Dementia Praecox ◽  
Intellectual Abilities ◽  
The Brain ◽  
Psychic Mechanisms

When Kraepelin originally defined and described dementia praecox, he assumed that it was due to some type of neural mechanism. He hypothesised that abnormalities could occur in a variety of brain regions, including the prefrontal, auditory, and language regions of the cortex. Many members of his department, including Alzheimer and Nissl, were actively involved in the search for the neuropathological lesions that would characterise schizophrenia. Although Kraepelin did not use the term ‘negative symptoms', he describes them comprehensively and states explicitly that he believes the symptoms of schizophrenia can be explained in terms of brain dysfunction:“If it should be confirmed that the disease attacks by preference the frontal areas of the brain, the central convolutions and central lobes, this distribution would in a certain measure agree with our present views about the site of the psychic mechanisms which are principally injured by the disease. On various grounds, it is easy to believe that the frontal cortex, which is specially well developed in man, stands in closer relation to his higher intellectual abilities, and these are the faculties which in our patients invariably suffer profound loss in contrast to memory and acquired ability.” Kraepelin (1919, p. 219)

Auditory-Somatosensory Multisensory Processing in Auditory Association Cortex: An fMRI Study

2002 ◽  
Vol 88 (1) ◽  
pp. 540-543 ◽  
Author(s):  
John J. Foxe ◽  
Glenn R. Wylie ◽  
Antigona Martinez ◽  
Charles E. Schroeder ◽  
Daniel C. Javitt ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Multisensory Integration ◽  
Multisensory Processing ◽  
Primary Auditory Cortex ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Association Cortex ◽  
Convergence Region ◽  
Auditory Cortices ◽  
Auditory Association Cortex

Using high-field (3 Tesla) functional magnetic resonance imaging (fMRI), we demonstrate that auditory and somatosensory inputs converge in a subregion of human auditory cortex along the superior temporal gyrus. Further, simultaneous stimulation in both sensory modalities resulted in activity exceeding that predicted by summing the responses to the unisensory inputs, thereby showing multisensory integration in this convergence region. Recently, intracranial recordings in macaque monkeys have shown similar auditory-somatosensory convergence in a subregion of auditory cortex directly caudomedial to primary auditory cortex (area CM). The multisensory region identified in the present investigation may be the human homologue of CM. Our finding of auditory-somatosensory convergence in early auditory cortices contributes to mounting evidence for multisensory integration early in the cortical processing hierarchy, in brain regions that were previously assumed to be unisensory.

scholarly journals SAT-606 Distribution of Beta Klotho Gene Expression in the Mouse Brain

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Bianca S Bono ◽  
Persephone A Miller ◽  
Nikita K Koziel Ly ◽  
Melissa J Chee
Keyword(s):  
Basolateral Amygdala ◽  
Piriform Cortex ◽  
Brain Regions ◽  
Brain Atlas ◽  
Hypothalamic Nucleus ◽  
Fibroblast Growth Factor 21 ◽  
Lateral Olfactory Tract ◽  
Hypothalamic Area ◽  
Low Levels ◽  
The Brain

Abstract Fibroblast growth factor 21 (FGF21) has emerged as a critical endocrine factor for understanding the neurobiology of obesity, such as by the regulation thermogenesis, food preference, and metabolism, as well as for neuroprotection in Alzheimer’s disease and traumatic brain injury. FGF21 is synthesized primarily by the liver and pancreas then crosses the blood brain barrier to exert its effects in the brain. However, the sites of FGF21 action in the brain is not well-defined. FGF21 action requires the activation of FGF receptor 1c as well as its obligate co-receptor beta klotho (KLB). In order to determine the sites of FGF21 action, we mapped the distribution of Klb mRNA by in situ hybridization using RNAscope technology. We labeled Klb distribution throughout the rostrocaudal axis of male wildtype mice by amplifying Klb hybridization using tyramine signal amplification and visualizing Klb hybridization using Cyanine 3 fluorescence. The resulting Klb signal appears as punctate red “dots,” and each Klb neuron may express low (1–4 dots), medium (5–9 dots), or high levels (10+ dots) of Klb hybridization. We then mapped individual Klb expressing neuron to the atlas plates provided by the Allen Brain Atlas in order to determine Klb distribution within the substructures of each brain region, which are defined by Nissl-based parcellations of cytoarchitectural boundaries. The distribution of Klb mRNA is widespread throughout the brain, and the brain regions analyzed thus far point to notable expression in the hypothalamus, amygdala, hippocampus, and the cerebral cortex. The highest expression of Klb was localized to the suprachiasmatic nucleus in the hypothalamus, which contained low and medium Klb-expressing neurons in the lateral hypothalamic area and ventromedial hypothalamic nucleus while low expressing Klb neurons were seen in the paraventricular and dorsmedial hypothalamic nucleus. Hippocampal Klb expression was limited to the dorsal region and largely restricted to the pyramidal cell layer of the dentate gyrus, CA3, CA2, and CA1 but at low levels only. In the amygdala, low and medium Klb expressing cells were seen in lateral amygdala nucleus while low levels were observed in the basolateral amygdala nucleus. Cortical Klb expression analyzed thus far included low Klb-expressing neurons in the olfactory areas, including layers 2 and 3 of piriform cortex and nucleus of the lateral olfactory tract. These findings are consistent with the known roles of FGF21 in the central regulation of energy balance, but also implicates potentially wide-ranging effects of FGF21 such as in executive functions.

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.

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