O2.2. HIPPOCAMPAL SUBFIELD MORPHOLOGY AND MYELINATION IN UNTREATED FIRST EPISODE PSYCHOSIS: A 7T MRI STUDY
Notice bibliographique
Résumé
The hippocampus and its subfields are considered putative markers in schizophrenia. In particular, earliest volume deficits in select subfields have been demonstrated in multiple studies and extending to the rest later in the disease. Recently, there is increasing interest in studying the white matter projections from the hippocampus including the alveus, fimbria, and fornix that envelop the hippocampal formation--while there is mixed evidence towards their involvement in psychosis. Here, we leveraged a novel neuroanatomical atlas and automated segmentation to study the hippocampal subfields (GM) and white matter (WM) regions in first-episode psychosis (FEP). Participants were recruited as part of a longitudinal neuroimaging project that follows changes in early psychosis during the course of treatment. 28 healthy control (HC) and 43 antipsychotic-naive FEP subjects were included. Subjects were assessed using DSM-5 criteria and the 8 item Positive and Negative Syndrome Scale (PANSS-8). All underwent magnetic resonance imaging at 7T using the MP2RAGE sequence to assess both grey matter and myelin content (qT1). High-resolution T1-weighted images (0.75mm3) were used to delineate the hippocampal subregions using the MAGeT Brain algorithm as previously validated. Volumes and qT1 in 9 structures (CA1, CA2/3, CA4/DG, SR/SL/SM, subiculum, and alveus, fimbria, fornix, mammillary bodies) were measured. Statistical analyses included the following: 1) Multiple linear regression with total hippocampal GM and WM volumes as dependent variables, examining the main effect of diagnosis accounting for age, gender as covariates, 2) Similarly, MANCOVA with subregion measures (volume and qT1) as dependent variables seeking the effect of diagnosis with age/gender covariates, 3) Partial least-squares (PLS) regression to determine the relationship between subregion measures and PANSS. Initial analyses included the entire cohort (28 HC, 43 FEP). We found significantly lower hippocampal GM (left: t= -2.33, p= 0.023, right: t=-2.64, p=0.010) and WM (left: t= -1.71, p=0.092, right: -2.351, p=0.022) volumes in FEP compared to HC. PLS regression (in the entire FEP cohort; N=43) identified two components that explained ~25% of the variance in total PANSS scores, with the first component significantly correlated with negative symptom scores (R=0.39, p=0.01), and second with positive symptom scores (R=0.31, p=0.04). We subsequently included only FEP subjects with a later diagnosis of schizophrenia, with more pronounced differences in GM (left: t=-2.24, p=0.030, right: t=-2.83, p=6.88E-03) and WM (left: t=-1.86, p=0.069, right: t=-3.319, p=1.77E-03). Subsequent analyses include only FEP-schizophrenia (25 FES). MANCOVA for subregion volumes and qT1 was not significant (p > 0.1), while post-hoc univariate testing showed significant contraction of the right CA4DG (t=-3.08, p=3.53E-03), stratum (t=-2.84, p=6.73E-03) and fimbria (t=-2.79, p=7.65E-03). In addition, we found qT1 increases mostly in the right stratum (t= 3.02, p=4.17E-03), CA2/CA3 (t=2.75, p=8.61E-03). For both volumes and qT1, less pronounced effects of diagnosis were seen in other subfields (p < 0.05). We replicated previous findings of selective reduction of hippocampal subfields, with an asymmetric preference towards the right. We additionally show contraction in white matter subregions with concurrent qT1 increases suggesting the possibility of myelin loss in early psychosis. Lastly, we demonstrate that these measures predict psychosis severity at baseline across diagnostic groups. Taken together, these findings indicate subfield specific abnormalities in the hippocampus as an anatomical feature of psychosis.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».