O2.2. HIPPOCAMPAL SUBFIELD MORPHOLOGY AND MYELINATION IN UNTREATED FIRST EPISODE PSYCHOSIS: A 7T MRI STUDY
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".