Thalamic medial dorsal nucleus atrophy in medial temporal lobe epilepsy: A VBM meta-analysis

Purpose: Medial temporal lobe epilepsy (MTLE) is associated with MTLE network pathology within and beyond the hippocampus. The purpose of this meta-analysis was to identify consistent MTLE structural change to guide subsequent targeted analyses of these areas. Methods: We performed an anatomic likelihood estimation (ALE) meta-analysis of 22 whole-brain voxel-based morphometry experiments from 11 published studies. We grouped these experiments in three ways. We then constructed a meta-analytic connectivity model (MACM) for regions of consistent MTLE structural change as reported by the ALE analysis. Key findings: ALE reported spatially consistent structural change across VBM studies only in the epileptogenic hippocampus and the bilateral thalamus; within the thalamus, the medial dorsal nucleus of the thalamus (MDN thalamus) represented the greatest convergence (Pb0.05 corrected for multiple comparisons). The subsequent MACM for the hippocampus and ipsilateral MDN thalamus demonstrated that the hippocampus and ipsilateral MDN thalamus functionally co-activate and are nodes within the same network, suggesting that MDN thalamic damage could result from MTLE network excitotoxicity. Significance: Notwithstanding our large sample of studies, these findings aremore restrictive thanprevious reports and demonstrate the utility of our inclusion filters and of recently modified meta-analyticmethods in approximating clinical relevance. Thalamic pathology is commonly observed in animal and human studies, suggesting it could be a clinically useful indicator. Thalamus-specific research as a clinical marker awaits further investigation

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p

Implementation errors in the GingerALE Software: Description and recommendations.

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc

Discovering Pathophysiologic Networks of Temporal Lobe Epilepsy Using the BrainMap Community Portal through the Texas Advanced Computing Center

Temporal lobe epilepsy (TLE) seizures cause regional damage, detectable on imaging of brain structure (VBM) and function (VBM). Damage is mediated by aberrant neuronal activity propagating along existing network architecture. TLE networks remain ill-defined and are of great interest to diagnostic and therapeutic development. Independent component analysis (ICA) can detect neural-networks by computing multi-variate co-occurrence patterns across a volume and is validated for coordinate-based meta-analysis (CBMA/Meta-ICA). Meta-ICA is methodologically distinct from mass-univariate meta-analytics (ALE) that simply detect robust regions/hubs of pathology. Although meta-ICA is typically used to extract canonical/healthy networks, we applied meta-ICA to VBM/VBP reports of TLE-pathology, to infer TLE-specific network anomalies. To identify TLE-networks, BrainMap Community Portal applications were used to access coordinate-results (Sleuth) of 74 experiments (n=1599), model coordinates as spatial probability distributions (weighted by sample-size) and apply ICA (Meta-ICA) at dimensions:d=1&2 (per sample-size restrictions), computing coordinate co-occurrence within and across experiments. TLE pathology-hubs were computed (GingerALE) separately as spatial convergence across studies (ALE), agnostic to within-study co-occurrence. Two anatomically distinct TLE-networks were identified (i.e. no overlap, excepting ALE hubs). Network-IC1 included brain-regions involved in language processing (speech-execution:Z=6.95; speech-cognition:Z=4.10) at d=2, with similar results (spatial-correlation:R=0.89) at d=1. Network-IC2 included brain-regions involved in emotion (reward:Z=4.76) and cognition (attention:Z=4.02; memory:Z=3.80). Meta-ICA implicated a superset of hub regions from GingerALE (IC1-tonsil; IC2-pulvinar, caudate, superior-temporal; Both-hippocampus, MDN-thalamus), uniquely identifying anterior nucleus, insula, supramarginal, and pre/paracentral gyri. Neither network matched canonical networks. VBM/VBP distribution to ICs was homogenous (χ2:p=0.07). Meta-ICA networks align with TLE symptom profiles. IC1 (verbal/visual) disruption can impair communication and cause visual hallucinosis. IC2 (limbic) aligns with social-emotional deficits; dyscognitive seizures disrupt cognition (attention/memory), impair awareness, and induce postictal amnesia. These findings reveal two novel TLE-networks, debut the first low-d meta-ICA detection of disease-networks, and highlight a BrainMap Community Portal use-case with implications in biomarker development within/beyond epilepsy pathologies.Texas Advanced Computing Center (TACC

The hippocampal network model: A transdiagnostic metaconnectomic approach

Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network. Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models. Key findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049). Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays. Keywords: Anatomic likelihood estimation, ALE, BrainMap, Functional covariance, Functional MRI, Gray matter density, Hippocampal network model, Hippocampus, Magnetic resonance imaging, MRI, Meta-analysis, Meta-analytic connectivity modeling, MACM, Structural covariance, Structural MRI, Voxel-based morphometry, VB

BrainMap VBM: An environment for structural meta-analysis

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches

Brain pathology recapitulates physiology: A network meta-analysis

Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic 'cost' significantly differs along this transdiagnostic/multimodal gradient

Meta-analytic connectivity and behavioral parcellation of the human cerebellum.

The cerebellum historically has been thought to mediate motor and sensory signals between the body and cerebral cortex, yet cerebellar lesions are also associated with altered cognitive behavioral performance. Neuroimaging evidence indicates that the cerebellum contributes to a wide range of cognitive, perceptual, and motor functions. Here, we used the BrainMap database to investigate whole-brain co-activation patterns between cerebellar structures and regions of the cerebral cortex, as well as associations with behavioral tasks. Hierarchical clustering was performed to meta-analytically identify cerebellar structures with similar cortical co-activation, and independently, with similar correlations to specific behavioral tasks. Strong correspondences were observed in these separate but parallel analyses of meta-analytic connectivity and behavioral metadata. We recovered differential zones of cerebellar co-activation that are reflected across the literature. Furthermore, the behaviors and tasks associated with the different cerebellar zones provide insight into the specialized function of the cerebellum, relating to high-order cognition, emotion, perception, interoception, and action. Taken together, these task-based meta-analytic results implicate distinct zones of the cerebellum as critically involved in the monitoring and mediation of psychological responses to internal and external stimuli

Neural architecture underlying classification of face perception paradigms

We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks