Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

Kerr, Wesley T.; Tatekawa, Hiroyuki; Lee, John K.; Karimi, Amir H.; Sreenivasan, Siddhika S.; O’Neill, Joseph; Smith, Jena M.; Hickman, L. Brian; Savić, Ivanka; Nasrullah, Nilab; Espinoza, Randall; Narr, Katherine L.; Salamon, Noriko; Beimer, Nicholas J.; Hadjiiski, Lubomir M.; Eliashiv, Dawn; Stacey, William C.; Engel, Jerome; Feusner, Jamie D.; Stern, John M.

doi:10.1016/j.yebeh.2022.108858

Cited by 11 publications

(3 citation statements)

References 175 publications

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“…Working memory performance was evaluated using the Y maze task, and logistic regression was employed with volume measurement as a continuous independent variable and percent alteration expressed as a proportion score, bound between the values of 0 and 1, as the dependent variable ( Long, 1997 ). To account for multiple comparisons, raw p -values from each analysis ( n = 6, 3, respectively) were adjusted using the Bonferroni-Holm method ( Holm, 1979 ), a correction commonly used in MRI analysis ( Kerr et al, 2022 ; Straub et al, 2023 ). We opted for this relatively conservative correction method to ensure greater specificity of our findings.…”

Section: Methodsmentioning

confidence: 99%

Brain structure and working memory adaptations associated with maturation and aging in mice

Clifford

Miles

Prevot

et al. 2023

Front. Aging Neurosci.

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IntroductionAs the population skews toward older age, elucidating mechanisms underlying human brain aging becomes imperative. Structural MRI has facilitated non-invasive investigation of lifespan brain morphology changes, yet this domain remains uncharacterized in rodents despite increasing use as models of disordered human brain aging.MethodsYoung (2m, n = 10), middle-age (10m, n = 10) and old (22m, n = 9) mice were utilized for maturational (young vs. middle-age) and aging-related (middle-age vs. old mice) comparisons. Regional brain volume was averaged across hemispheres and reduced to 32 brain regions. Pairwise group differences in regional volume were tested using general linear models, with total brain volume as a covariate. Sample-wide associations between regional brain volume and Y-maze performance were assessed using logistic regression, residualized for total brain volume. Both analyses corrected for multiple comparisons. Structural covariance networks were generated using the R package “igraph.” Group differences in network centrality (degree), integration (mean distance), and segregation (transitivity, modularity) were tested across network densities (5–40%), using 5,000 (1,000 for degree) permutations with significance criteria of p < 0.05 at ≥5 consecutive density thresholds.ResultsWidespread significant maturational changes in volume occurred in 18 brain regions, including considerable loss in isocortex regions and increases in brainstem regions and white matter tracts. The aging-related comparison yielded 6 significant changes in brain volume, including further loss in isocortex regions and increases in white matter tracts. No significant volume changes were observed across either comparison for subcortical regions. Additionally, smaller volume of the anterior cingulate area (χ2 = 2.325, pBH = 0.044) and larger volume of the hippocampal formation (χ2 = −2.180, pBH = 0.044) were associated with poorer cognitive performance. Maturational network comparisons yielded significant degree changes in 9 regions, but no aging-related changes, aligning with network stabilization trends in humans. Maturational decline in modularity occurred (24–29% density), mirroring human trends of decreased segregation in young adulthood, while mean distance and transitivity remained stable.Conclusion/ImplicationsThese findings offer a foundational account of age effects on brain volume, structural brain networks, and working memory in mice, informing future work in facilitating translation between rodent models and human brain aging.

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Section: Methodsmentioning

confidence: 99%

Brain structure and working memory adaptations associated with maturation and aging in mice

Clifford

Miles

Prevot

et al. 2023

Front. Aging Neurosci.

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“…There has been much research interest in neuroanatomic and functional underpinnings of PNES in the brain. Kerr et al [30] reported that, compared to seizure-naive controls (patients with depression, anxiety, and OCD), patients with PNES had a bilaterally thinner superior temporal cortex, thicker left lateral occipital cortex, and greater left cerebellar white-matter volume. Using similar imaging methods, Zelinski et al [31] investigated the relationship of gray matter variations with clinical features.…”

Section: Neurobiological Understanding Of Psychogenic Nonepileptic Se...mentioning

confidence: 99%

An update on psychogenic nonepileptic seizures

Vijay,

Reuber

2024

Current Opinion in Neurology

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Purpose of review The understanding of psychogenic nonepileptic seizures (PNES) has advanced steadily over recent decades. This update summarizes new insights from the last three years. Recent findings The process of diagnosing PNES has shifted from the exclusion of epilepsy to one based on the recognition of typical clinical features. While the diagnosis cannot rely on any single feature in isolation, a range of semiological features characterising PNES are now recognised and a number of studies hint at the potential for machine learning and AI to improve the diagnostic process. Advances in data processing and analysis may also help to make sense of the heterogeneity of PNES populations demonstrated by recent studies focussing on aetiology and patient subgroups. It is now clear that PNES are associated with high rates of mental and physical comorbidities and premature death, highlighting that they are only one manifestation of a complex disorder extending beyond the nervous system and the seizures themselves. Summary PNES are now understood as a manifestation of dysfunction in interacting brain networks. This understanding provides an explanation for the psychopathological and semiological heterogeneity of PNES patient populations. New insights into medical comorbidities and increased rates of premature death call for more research into associated pathological processes outside the nervous system.

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“…In research settings, it is customary to recruit "healthy", "cognitively normal", or "unaffected" participants explicitly for this purpose. For clinically-acquired MRIs, a control group might be composed of demographically-matched patients who underwent brain MRIs to rule out serious neuropathology and were found to have unremarkable MRIs (Kerr et al 2022). A critical and unresolved question is whether the difference in ascertainment process between research-controls and clinical-controls biases inferences about patient groups of interest.…”

Section: Introductionmentioning

confidence: 99%

Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

Schabdach

Schmitt

Sotardi

et al. 2023

Preprint

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Background: Brain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs. Purpose: To develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies. Materials and Methods: We curated a set of clinical MRI Scans with Limited Imaging Pathology (SLIP) - so-called "clinical controls" - from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs. Results: The curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts. Conclusion: Brain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.

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Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

Cited by 11 publications

References 175 publications

Brain structure and working memory adaptations associated with maturation and aging in mice

Brain structure and working memory adaptations associated with maturation and aging in mice

An update on psychogenic nonepileptic seizures

Brain growth charts of “clinical controls” for quantitative analysis of clinically acquired brain MRI

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