False positive rates in Voxel-based Morphometry studies of the human brain: Should we be worried?

Scarpazza, Cristina; Tognin, Stefania; Frisciata, Silvia; Sartori, Giuseppe; Mechelli, Andrea

doi:10.1016/j.neubiorev.2015.02.008

Cited by 77 publications

(62 citation statements)

References 40 publications

(78 reference statements)

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“…First, studies are needed to determine whether the novel pattern of alterations we observed can be replicated, and whether they change over the course of drug treatment, acute clinical stabilization and over the longer term course of the illness. Fifth, we note that the VBM procedure we used is susceptible to false positive results [Scarpazza et al, 2015], and future studies are needed to extend our findings using other surface-based morphological measures, such as cortical thickness or surface area to validate and clarify our findings. It has been recently documented that similarity-based patterns of cortical morphology may arise from systematic functional co-activation and/or mutually trophic reinforcement, genetic mediated brain maturation, as well as experience-related plasticity [Alexander-Bloch et al, 2013;Evans, 2013].…”

Section: Limitationsmentioning

confidence: 80%

Anomalous single‐subject based morphological cortical networks in drug‐naive, first‐episode major depressive disorder

Chen

Kendrick

Wang

et al. 2017

Human Brain Mapping

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Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group-level data. Such disruptions have not yet been identified in single-patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross-sectional structural brain network study of 33 treatment-naive, first-episode MDD patients and 33 age-, gender-, and education-matched healthy controls (HCs). Weighted graph-theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole-brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default-mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex-striatum-thalamus-cerebellum and thalamo-hippocampal circuitry. MDD-related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment-naive, first episode MDD patients. Circuit-specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482-2494, 2017. © 2017 Wiley Periodicals, Inc.

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Section: Limitationsmentioning

confidence: 80%

Anomalous single‐subject based morphological cortical networks in drug‐naive, first‐episode major depressive disorder

Chen

Kendrick

Wang

et al. 2017

Human Brain Mapping

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“…Our findings should be considered preliminary, given group sizes that were comparatively small for VBM studies. However, while single case VBM studies are extremely susceptible to high false positive rates and thus should be avoided, a recent study revealed no significant impact of sample size (n=8, 12, and 16) on detecting false positive group differences (Scarpazza et al, 2015). We also did not include a comparison group of non-deployed veterans.…”

Section: Discussionmentioning

confidence: 99%

Burning odor-elicited anxiety in OEF/OIF combat veterans: Inverse relationship to gray matter volume in olfactory cortex

Cortese¹,

McConnell

Froeliger

et al. 2015

Journal of Psychiatric Research

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Despite the anatomical overlap between the brain’s fear/threat and olfactory systems, a very limited number of investigations have considered the role of odors and the central olfactory system in the pathophysiology of PTSD. The goal of the present study was to assess structural differences in primary and secondary olfactory cortex between combat veterans with and without PTSD (CV+PTSD, CV−PTSD, respectively). An additional goal was to determine the relationship between gray matter volume (GMV) in olfactory cortex and the distressing properties of burning-related odors. A region of interest voxel-based morphometric (VBM) approach was used to measure GMV in olfactory cortex in a well-characterized group of CV+PTSD (n=20) and CV−PTSD (n=25). Prior to the MRI exam, combat-related (i.e., burning rubber) and control odors were systematically sampled and rated according to their potential for eliciting PTSD symptoms. Results showed that CV+PTSD exhibited significantly reduced GMV in anterior piriform (primary olfactory) and orbitofrontal (secondary olfactory) cortices compared to CV−PTSD (both p<.01). For the entire group, GMV in bilateral anterior piriform cortex was inversely related to burning rubber odor-elicited memories of trauma (p<.05). GMV in orbitofrontal cortex was inversely related to both clinical and laboratory measures of PTSD symptoms (all p<.05). In addition to replicating an established inverse relationship between GMV in anxiety-associated brain structures and PTSD symptomatology, the present study extends those findings by being the first report of volumetric decreases in olfactory cortex that are inversely related to odor-elicited PTSD symptoms. Potential mechanisms underlying this finding are discussed.

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“…Furthermore, automated imaging analysis output of identical digital MR data may differ depending on the operating system used to run the program (Gronenschild et al 2012). When comparing studies and findings it remains critical that attention be directed to matters related to sample size, degree of smoothing and image modulation to keep false positive rates low (Scarpazza et al 2015). So automated methods provide great opportunity to assess large datasets Note that through this method the major regions of interest and brain nuclei may be identified and quantified but still require operator input and vigilance with regards to quality assurance and, to date, unresolved issues remain that relate to generalizability of findings across different acquisition sites and MR platforms (Chalavi et al 2012;Diaz-deGrenu et al 2014;Durand-Dubief et al 2012;Liem et al 2015;Nakamura et al 2014).…”

Section: Overview Of Structural Neuroimaging Analysesmentioning

confidence: 99%

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

Bigler

2015

Neuropsychol Rev

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Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

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False positive rates in Voxel-based Morphometry studies of the human brain: Should we be worried?

Cited by 77 publications

References 40 publications

Anomalous single‐subject based morphological cortical networks in drug‐naive, first‐episode major depressive disorder

Anomalous single‐subject based morphological cortical networks in drug‐naive, first‐episode major depressive disorder

Burning odor-elicited anxiety in OEF/OIF combat veterans: Inverse relationship to gray matter volume in olfactory cortex

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

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