{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T16:34:53Z","timestamp":1780072493426,"version":"3.54.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 NS120976"],"award-info":[{"award-number":["R01 NS120976"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 NS111022"],"award-info":[{"award-number":["R01 NS111022"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 NS117568"],"award-info":[{"award-number":["R01 NS117568"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["U01 NS093650"],"award-info":[{"award-number":["U01 NS093650"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional\u2013behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic\u2013clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.<\/jats:p>","DOI":"10.1093\/braincomms\/fcad095","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T23:45:32Z","timestamp":1680219932000},"source":"Crossref","is-referenced-by-count":10,"title":["The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy"],"prefix":"10.1093","volume":"5","author":[{"given":"Aaron F","family":"Struck","sequence":"first","affiliation":[{"name":"Department of Neurology, University of Wisconsin-Madison , Madison, WI 53726 , USA"},{"name":"Department of Neurology, William S. Middleton Veterans Administration Hospital , Madison, WI 53705 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4365-9895","authenticated-orcid":false,"given":"Camille","family":"Garcia-Ramos","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veena A","family":"Nair","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Prabhakaran","sequence":"additional","affiliation":[{"name":"Department of Neurology, Medical College of Wisconsin , Milwaukee, WI 53226 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Dabbs","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melanie","family":"Boly","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa L","family":"Conant","sequence":"additional","affiliation":[{"name":"Department of Neurology, Medical College of Wisconsin , Milwaukee, WI 53226 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2233-5640","authenticated-orcid":false,"given":"Jeffrey R","family":"Binder","sequence":"additional","affiliation":[{"name":"Department of Neurology, Medical College of Wisconsin , Milwaukee, WI 53226 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary E","family":"Meyerand","sequence":"additional","affiliation":[{"name":"Department of Medical Physics, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0133-4427","authenticated-orcid":false,"given":"Bruce P","family":"Hermann","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Wisconsin-Madison , Madison, WI 53726 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"issue":"2","key":"2023040812292599600_","first-page":"457","article-title":"Neuropsychiatric and cognitive comorbidities in epilepsy","volume":"28","author":"Mula","year":"2022","journal-title":"Continuum (Minneap Minn)"},{"issue":"12","key":"2023040812292599600_","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1038\/s41582-021-00555-z","article-title":"Neurobehavioural comorbidities of epilepsy: Towards a network-based precision taxonomy","volume":"17","author":"Hermann","year":"2021","journal-title":"Nat Rev Neurol"},{"key":"2023040812292599600_","doi-asserted-by":"crossref","DOI":"10.1177\/10738584221076133","article-title":"Neurobehavioral and clinical comorbidities in epilepsy: The role of white matter network disruption","author":"Stasenko","year":"2022 22","journal-title":"Neuroscientist"},{"key":"2023040812292599600_","author":"World Health Organization"},{"issue":"2","key":"2023040812292599600_","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.yebeh.2012.06.016","article-title":"Epilepsy across the spectrum: Promoting health and understanding. 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