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Hippocampal network dysfunction in early psychosis: a 2-year longitudinal study

Open AccessPublished:October 25, 2022DOI:https://doi.org/10.1016/j.bpsgos.2022.10.002

      ABSTRACT

      Background

      Hippocampal abnormalities are among the most consistent findings in schizophrenia. Numerous studies have reported deficits in hippocampal volume, function, and connectivity in the chronic stage of illness. While hippocampal volume and function deficits are also present in the early stage of illness, there is mixed evidence of both higher and lower functional connectivity. Here, we use graph theory to test the hypothesis that hippocampal network connectivity is broadly lowered in early psychosis and progressively worsens over two years.

      Methods

      We examined longitudinal resting-state functional connectivity in 140 participants (68 individuals in the early stage of psychosis, 72 demographically similar healthy control individuals). We used an anatomically-driven approach to quantify hippocampal network connectivity at two levels: 1) a core hippocampal-medial temporal lobe cortex (MTLC) network; and 2) an extended hippocampal-cortical network. Group and time effects were tested in a linear mixed effects model.

      Results

      Early psychosis patients showed elevated functional connectivity in the core hippocampal-MTLC network, but contrary to our hypothesis, did not show alterations within the broader hippocampal-cortical network. Hippocampal-MTLC network hyperconnectivity normalized longitudinally and predicted improvement in positive symptoms, but was not associated with increasing illness duration.

      Conclusions

      These results show abnormally elevated functional connectivity in a core hippocampal-MTLC network in early psychosis, suggesting selectively increased hippocampal signaling within a localized cortical circuit may be a marker of the early stage of psychosis. Hippocampal-MTLC hyperconnectivity could have prognostic and therapeutic implications.

      Keywords

      INTRODUCTION

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      ), although it remains unclear whether this shift in connectivity patterns is also present in the hippocampal network.
      One reason for this gap in knowledge may be due to the distinct architecture of hippocampal connections. The hippocampus binds sensory information from distributed parts of the cortex to form holistic representations of experiences. Polymodal sensory information converges on the hippocampus via medial temporal lobe cortices (MTLC; perirhinal, parahippocampal, and entorhinal) (
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      To address this gap, we used an anatomically-guided approach to examine hippocampal network connectivity in both a core hippocampal-MTLC network and an extended hippocampal-cortical network in a cohort of early psychosis patients. To characterize network interactions, we used the graph theory metric of modularity, an index of the balance between functional integration and segregation of networks. Higher modularity scores indicate greater cohesion of into a distinct network, while lower modularity scores indicate lower cohesion. Using a similar approach, we previously demonstrated decreased modularity and broad hypoconnectivity in hippocampal networks in chronic schizophrenia patients (
      • Avery S.N.
      • Rogers B.P.
      • Heckers S.
      Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia.
      ). Because hippocampal deficits are already prominent at the earliest stages of illness, we hypothesized that early psychosis would also be associated with decreased modularity and widespread network hypoconnectivity. To determine whether hippocampal network connectivity changes during the early stage of illness, we used a prospective 2-year longitudinal study design to examine hippocampal network function across the initial years of illness. Patients completed a neuroimaging assessment within the first two years of illness and follow-up assessments every 8 months for two years. We hypothesized early psychosis patients would show greater hippocampal network hypoconnectivity over two years, in line with findings of broad hypoconnectivity in chronic illness.

      METHODS AND MATERIALS

      Participants

      Participants (N=140) were 68 individuals in the early stage of a psychotic disorder and 72 healthy control participants enrolled between May 2013 and February 2018 into a prospective 2-year longitudinal neuroimaging study. Most participants (57 early psychosis, 66 healthy control) were included at two or more visits. Details regarding participant attrition are included in the supplement. Groups were recruited with the goal to have similar age, gender, race and parental education (Table 1). Early psychosis patients included in the analysis had similar demographic and clinical characteristics as those who were excluded.
      Table 1Participant characteristics.
      DemographicsEarly Psychosis, n = 68Healthy Control, n = 72Early Psychosis vs. Healthy Control
      Mean ± SDMean ± SDStatisticp
      Age at enrollment, years21 ± 3.322 ± 2.8F1,139 = 0.76.38
      Sex, % male79%74%Χ21 = 0.65.42
      Race, White/Black/Other52/15/156/12/4Χ22 = 2.17.34
      Ethnicity, % Non-Hispanic99%93%Χ22 = 2.55.11
      Handedness, % right94%92%Χ21 = 0.32.57
      Premorbid IQ, WTAR103 ± 15.0113 ± 9.7F1,130 = 19.67a<.001*
      Participant’s education, years13 ± 2.215 ± 1.9F1,139 = 13.66<.001*
      Parental education, years15 ± 2.815 ± 2.3F1,139 = 0.54.46
      Clinical characteristicsStudy entry, n = 52b2-year follow-up, n = 56Study entry vs. 2-year
      Mean ± SDRangeMean ± SDRangeStatisticp
      Diagnosis, SZF/SZ/BPc35/14/315/41/0
      Duration of illness, months7.2 ± 5.91 – 2431.8 ± 5.725 – 48
      CPZ, mg337 ± 155.975 – 750299 ± 197.250 – 879F1,79 = 0.93.34
      Antipsychotic treatment, %85%64%Χ21 = 5.80.02*
      PANSS Total66 ± 20.734 – 11452 ± 15.133 – 104F1,107 = 15.32<.001*
      PANSS-Positive16 ± 7.07 – 3613 ± 4.77 – 27F1,107 = 7.68.007*
      PANSS-Negative17 ± 7.17 – 3312 ± 6.27 – 32F1,107 = 14.55<.001*
      PANSS-General32 ± 9.618 – 5927 ± 7.017 – 46F1,107 = 11.85.001*
      HAM-D11 ± 8.01 – 297 ± 6.30 – 28F1,106 = 9.42d.003*
      YMRS2 ± 4.10 – 192 ± 3.00 – 15F1,106 = 0.31d.58
      Demographic data are presented for included study participants. Note: WTAR, Wechsler Test of Adult Reading; SZF, schizophreniform; SZ, schizophrenia; BP, bipolar disorder with psychotic features; CPZ, chlorpromazine equivalent; PANSS, Positive and Negative Syndrome Scale; HAM-D, Hamilton Depression Rating Scale -17 item; YMRS, Young Mania Rating Scale. Asterisk (*) denotes significant p-values (p ≤ 0.05).
      a WTAR scores are reported for native English speakers (early psychosis, n = 66; healthy control, n = 65).
      b All data meeting quality thresholds were included. Exclusions are detailed by visit and group in the supplement.
      c All participants met criteria for a schizophrenia-spectrum disorder diagnosis at 2-year follow-up.
      d One patient missing HAM-D and YMRS at study entry.
      Patients in the early stage of a psychotic disorder (< 2 years following psychosis onset) were recruited from the Vanderbilt Psychiatric Hospital inpatient units and outpatient clinics. To specifically target early neuropathology (
      • Newton R.
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      ), the majority of early psychosis patients were recruited during the initial months following onset (mean = 7 months ± 6, range = 1-24 months). Diagnoses were determined using the Structured Clinical Interview for the DSM-IV-TR (SCID) (

      First M, Spitzer RRL, Gibbon M, Williams J (2002): Structured Clinical Interview for the DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute.

      ,

      American Psychiatric Association (2013): Diagnostic and statistical manual of mental disorders, 5th ed. Washington, DC.

      ), augmented by extensive review of all available medical records, and finalized by a senior psychiatrist (SH) during diagnostic consensus meetings. Patients were required to meet criteria A for schizophrenia at study entry. Only patients with either schizophreniform disorder or schizophrenia/schizoaffective disorder (referred to as schizophrenia going forward) at 2-year follow-up were included in this analysis. Healthy control participants were recruited from the community via advertisements. Exclusion criteria for all participants were < 16 or > 65 years of age, premorbid IQ < 75, a history of significant head injury, major medical or neurological illness, any contraindications for MRI scanning, substance abuse within the past month, and uncorrected vision deficits. In addition, healthy control participants were excluded for any history of Axis I disorders or prior psychotropic medication use, or a first-degree relative with a psychotic illness.
      Data from participants in this cohort have been included in previous reports (
      • McHugo M.
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      Hippocampal volume in early psychosis: a 2-year longitudinal study.
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      Anterior hippocampal dysfunction in early psychosis: A 2-year follow-up study.
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      • Armstrong K.
      • McHugo M.
      • Vandekar S.
      • JU Blackford
      • Woodward N.D.
      • Heckers S.
      Relational Memory in the Early Stage of Psychosis: A 2-Year Follow-up Study.
      ,
      • Armstrong K.
      • Avery S.
      • JU Blackford
      • Woodward N.
      • Heckers S.
      Impaired associative inference in the early stage of psychosis.
      ,
      • Avery S.N.
      • Armstrong K.
      • JU Blackford
      • Woodward N.D.
      • Cohen N.
      • Heckers S.
      Impaired relational memory in the early stage of psychosis.
      ,
      • Suh D.Y.
      • Vandekar S.N.
      • Heckers S.
      • Avery S.N.
      Visual exploration differences during relational memory encoding in early psychosis.
      ), but the functional connectivity analyses presented here are novel. All participants provided written informed consent and received monetary compensation for their time. The study was approved by the Vanderbilt University Institutional Review Board, Nashville, TN.

      Clinical measures

      Diagnostic assessments were collected during in-person interviews at study entry and 2-year visits. The onset of psychosis symptoms was determined using the Symptom Onset Scale (SOS) (
      • Perkins D.O.O.
      • Leserman J.
      • Jarskog L.F.F.
      • Graham K.
      • Kazmer J.
      • Lieberman J.A.A.
      Characterizing and dating the onset of symptoms in psychotic illness: the Symptom Onset in Schizophrenia (SOS) inventory.
      ). The duration of psychosis was calculated as the time between symptom onset and study enrollment. Chlorpromazine (CPZ) equivalent doses were calculated for patients on antipsychotic medication (
      • Gardner D.M.
      • Murphy A.L.
      • O’Donnell H.
      • Centorrino F.
      • Baldessarini R.J.
      International consensus study of antipsychotic dosing.
      ,
      • Leucht S.
      • Samara M.
      • Heres S.
      • Patel M.X.
      • Woods S.W.
      • Davis J.M.
      Dose equivalents for second-generation antipsychotics: The minimum effective dose method.
      ). Premorbid IQ was estimated using the Wechsler Test of Adult Reading (WTAR; Psychological Corporation, 2001) (
      • Dykiert D.
      • Deary I.J.
      Retrospective validation of WTAR and NART scores as estimators of prior cognitive ability using the Lothian Birth Cohort 1936.
      ,

      Dalby JT, Williams R (1986): Preserved reading and spelling ability in psychotic disorders. Psychol Med. 16: 171–175.

      ). Clinical symptom severity was assessed at each study visit using the Positive and Negative Syndrome Scale (PANSS) (
      • Kay S.R.
      • Fiszbein A.
      • Opler L.A.
      • Flszbeln A.
      • Qpjer L.A.
      • Fiszbein A.
      • et al.
      The positive and negative syndrome scale (PANSS) for schizophrenia.
      ,
      • Kay S.R.
      • Opler L.A.
      • Lindenmayer J.P.
      Reliability and validity of the positive and negative syndrome scale for schizophrenics.
      ), the Hamilton Depression Rating Scale (HAM-D) (
      • Hamilton M.
      A rating scale for depression.
      ), and the Young Mania Rating Scale (YMRS) (
      • Young R.C.
      • Biggs J.T.
      • Ziegler V.E.
      • Meyer D.A.
      A rating scale for mania: reliability, validity and sensitivity.
      ). Clinical and cognitive characteristics are reported in Table 1 and Supplementary Table 3.

      Imaging data

      Acquisition and preprocessing. High resolution T1-weighted structural and 7-minute echo-planar resting-state functional magnetic resonance imaging (rs-fMRI) data were collected at each study visit. Imaging data were acquired on two identical 3T Philips Intera Achieva MRI scanners using a 32-channel head coil at the Vanderbilt University Institute for Imaging Science. Images were processed on the Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT platform (
      • Harrigan R.L.
      • Yvernault B.C.
      • Boyd B.D.
      • Damon S.M.
      • Gibney K.D.
      • Conrad B.N.
      • et al.
      Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.
      ) in MATLAB (version 2018a) using SPM12 (http://www.fil.ion.ucl.ac.uk/spm). Resting-state data were motion corrected, coregistered to the subject’s structural image, and normalized to an MNI T1 template image (see supplement for full preprocessing details).
      Data quality. Data quality and exclusions are detailed in the supplement. Overall motion in the sample was low (median framewise displacement (FD) = .042 mm) and was higher in healthy control participants than patients (p = .002). To ensure differences did not influence graph theory comparisons, FD was included as a covariate of no interest for all between-group comparisons.

      Modularity analysis

      Regions of interest. Hippocampal network connectivity was calculated for two pre-defined networks: 1) a core hippocampal-MTLC network consisting of the hippocampus, rhinal cortex, and parahippocampal cortex (
      • Squire L.R.
      • Stark C.E.L.
      • Clark R.E.
      The medial temporal lobe.
      ); and 2) an extended hippocampal-cortical network consisting of functionally-connected prefrontal, posterior parietal, temporal, thalamic, and amygdala regions that interact with the core hippocampal network to support hippocampally-guided behavior (
      • Libby L.A.
      • Ekstrom A.D.
      • Daniel Ragland J.
      • Ranganath C.
      Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging.
      ,
      • Ranganath C.
      • Ritchey M.
      Two cortical systems for memory-guided behaviour.
      ,

      Ritchey M, Libby LA, Ranganath C (2015): Cortico-hippocampal systems involved in memory and cognition: The PMAT framework, 1st ed. Prog Brain Res. (Vol. 219), Elsevier B.V. doi: 10.1016/bs.pbr.2015.04.001.

      ). The hippocampal-MTLC network had 12 regions of interest (ROIs) divided into two a priori modules/communities (left and right, separately). The hippocampal-cortical network had 66 ROIs divided into 4 modules/communities (left- right and anterior-posterior, separately). Community assignments are detailed by anatomical brain region in Supplementary Table 1 and visualized in Figure 1. To provide a comparison to the hippocampal network, connectivity was also calculated for the visual network(
      • Thomas Yeo B.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ) (Supplementary Table 2). The visual network was chosen in light of considerable visual processing (
      • Green M.F.
      • Lee J.
      • Wynn J.K.
      • Mathis K.I.
      Visual masking in schizophrenia: Overview and theoretical implications.
      ) and visual ROI connectivity (
      • van de Ven V.
      • Rotarska Jagiela A.
      • Oertel-Knöchel V.
      • Linden D.E.J.
      Reduced intrinsic visual cortical connectivity is associated with impaired perceptual closure in schizophrenia.
      ,
      • Sharma A.
      • Kumar A.
      • Singh S.
      • Bhatia T.
      • Beniwal R.P.
      • Khushu S.
      • et al.
      Altered resting state functional connectivity in early course schizophrenia.
      ) deficits in schizophrenia.
      Figure thumbnail gr1
      Figure 1(A) Hippocampal networks are visualized on medial and lateral brain surfaces. (B) Area under the curve (AUC) of modularity values across sparsity thresholds (80 – 99%) are plotted as lsmean by group and time. Early psychosis patients had higher modularity than healthy control participants in the hippocampal-MTLC network driven by elevated modularity at study entry (t0). (C) Modularity values increased at higher sparsity thresholds across participants and for both hippocampal networks. Elevations in early psychosis patients were greater at higher thresholds. (D) Modularity values in the hippocampal-MTLC network significantly decreased over time in early psychosis patients, with the largest decrease occurring between study entry (t0) and 8-month follow-up (t1). Modularity remained stable from 8-month through 2-year follow-up (t1-t3). Error bars are 95% confidence intervals. Asterisks indicate significant effects.
      Network construction. Networks were calculated for a 66 x 66 region connectivity matrix. For each subject, temporal connectivity was obtained by extracting the average time-series for each ROI and calculating the ROI-to-ROI Pearson’s correlation coefficients. To remove potential sources of noise, fMRI signal was bandpass filtered (0.01 to 0.10 Hz) and 12 motion parameters, 6 principle components of white matter and CSF, and mean gray matter timeseries signal were removed (
      • Burgess G.C.
      • Kandala S.
      • Nolan D.
      • Laumann T.O.
      • Power J.D.
      • Adeyemo B.
      • et al.
      Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project.
      ). Correlation coefficients were Fischer r-to-z transformed and corrected for the number of time points. Both positive and negative connections were considered connectivity in brain networks. The absolute value of connections was calculated to create a non-negative graph matrix. Connectivity matrices were thresholded for noise (r ≥ .2) and binarized to create unweighted networks. The number of retained edges was similar between groups (p = .99). Functional connectivity matrices are displayed in Supplementary Figure 2.
      Network architecture. The architecture of hippocampal networks was investigated as described below. First, communities were assigned a priori as shown in Supplementary Table 1 and modularity (Q), or cohesion, of each network was calculated according to Newman’s metric (
      • Newman M.E.J.
      Modularity and community structure in networks.
      ). Second, to examine communication within hippocampal networks, we calculated characteristic path length, global efficiency, and degree density. Short characteristic path length, high global efficiency, and low degree density indicate ability to communicate quickly across the network as a whole (
      • Achard S.
      • Bullmore E.
      Efficiency and cost of economical brain functional networks.
      ,
      • Bullmore E.
      • Sporns O.
      The economy of brain network organization.
      ). Finally, we calculated the nodal descriptive statistics of degree (number of connected edges) and local efficiency to identify ROIs with altered nodal properties associated with module integrity. To evaluate the stability of networks over connection strengths, graph matrices were thresholded 80-99% sparsity, corresponding to values ranging from mild to strong connectivity (|r| ∼ .34-.57). For all network measures, area under the curve (AUC) across sparsity levels and served as the dependent variables in the models described below. Visual network modularity was calculated using methods described above (see Supplement for full details).

      Statistical analysis

      Statistical analyses were conducted in Statistical Analysis System (SAS Studio, version 3.8). Hippocampal network modularity served as the dependent variable in the primary analysis. Effects of group, time, and the interaction of group by time were modeled using linear mixed effects analyses, with group included as a fixed factor, time included as a repeated factor (t0 = study entry, t1 = t0 + 8 months, t2 = t0 + 16 months, t3 = t0 + 24 months), and participant as a random factor. FD, race, and scanner were included as covariates of no interest. For linear mixed effects models, hypothesis tests were performed using Satterthwaite’s adjustment for degrees of freedom and Cohen’s d effect sizes were calculated according to (
      • Vandekar S.
      • Tao R.
      • Blume J.
      A Robust Effect Size Index.
      ). Secondary analyses of network communication (characteristic path length, global efficiency, and degree density) and nodal characteristics (degree and local efficiency) were conducted using the primary model described above, with each serving as the dependent variable. Three exploratory linear mixed effects analyses were conducted to examine associations between clinical features and hippocampal network modularity, with modularity entered as the dependent variable, clinical feature (duration of illness, PANSS subscale scores, or HAM-D score) and time included as the independent variable, and FD, race, and scanner included as covariates of no interest. Duration of illness models did not include a time covariate. Spearman correlations tested for associations between modularity, demographic variables, and clinical features within visits. Associations were considered significant at p ≤ .05.

      RESULTS

      Demographic and clinical features

      Demographic and clinical characteristics are presented in Table 1. For simplicity, comparisons of study entry vs. 2-year clinical characteristics are presented; comparisons across all four study visits produced similar results and can be found in Supplementary Table 3. The early psychosis sample was young (mean age = 21, range = 16 – 31), primarily male (79%), white (76%), and educated (mean years of education = 13, range = 9 – 22). Psychosis and depression symptom severity decreased significantly over time (ps ≤ .007). Fewer patients were taking antipsychotic medication at follow-up visits compared to study entry (p = .02), although among those taking medication, chlorpromazine equivalent dosage was similar across visits (p = .34).

      Hippocampal networks

      Modularity. Modularity values were higher in early psychosis patients than healthy control participants, with significant differences detected in the hippocampal-MTLC network (F1,136 = 3.95, β = -0.005, StdErr = 0.003, d = 0.29, p = 0.05; Figure 1). Higher modularity in patients was largely driven by elevated group values at study entry (t0, control vs. patient: t = -1.77, d = .25, p = .08); later time points were similar across groups (t1 – t3, control vs. patient: ps ≥ .39). Hippocampal-MTLC network modularity decreased over time in all participants (F3,240 = 2.79, β = 0.014, StdErr = 0.005, d = 0.39, p = 0.04; no group x time interaction), with the largest decrease occurring between t0 and t1 (t0 vs. t1: t = 3.02, p = .003; t1 vs. t2: t = -1.53, p = .13; t2 vs. t3: t = 0.59, p = .56). In contrast, modularity of the hippocampal-cortical network was similar between groups and did not differ over time (ps ≥ .12). In comparison, visual network modularity was stably elevated in early psychosis patients (F1,136 = 10.27, β = -.009, StdErr = 0.003, d = 0.60, p = .002; no time or time x group interaction, ps ≥ .59). Comparing elevations at study entry, between-group effects were similar for the hippocampal-MTLC (d = .25) and visual networks (d = .30) (Supplementary Results).
      Network characteristics. To understand the dynamics that contribute to elevated hippocampal-MTLC network modularity in early psychosis, we next examined measures of network communication. Early psychosis patients had a greater number of connections per node (degree: F1,136 = 7.38, β = -0.17, StdErr = 0.08, d = 0.42, p = .007) and greater local efficiency of connections (F1,136 = 12.23, β = -0.01, StdErr = 0.004, d = 0.56, p < .001) compared to healthy control participants (Figure 2). The overall density of connections also tended to be higher in patients than healthy control participants (degree density: F1,136 = 3.66, β = -0.016, StdErr = 0.01, d = 0.27, trend p = .06; Supplementary Figure 5). Similar to modularity, local efficiency was highest at study entry and lower at all subsequent visits (F3,242 = 9.28, β = 0.03, StdErr = 0.005, d = 0.84, p < .001). Hippocampal-MTLC modularity was strongly correlated with the number and local efficiency of connections (ps ≤ .001). Full statistical results are detailed in the supplement. Together, these findings indicate the hippocampal-MTLC network is more densely integrated in early psychosis patients than in healthy individuals.
      Figure thumbnail gr2
      Figure 2(A) Network graphs are displayed for the early psychosis and healthy control groups at study entry (t0). Graphs were drawn in Gephi (https://gephi.org/) using the Yifan Hu Multilevel layout algorithm on the weighted network at 90% sparsity. Node sizes and connection weights are scaled by number and weight of connections, respectively. Node color denotes the assigned network. To reduce the number of connections visualized, displayed connections are present in ≥ 20% of individuals. (B) On average, mean degree and local efficiency of the hippocampal-MTLC network was higher in early psychosis patients than healthy control participants. Error bars are 95% confidence intervals.

      Clinical correlates of hippocampal network modularity

      To understand why hippocampal-MTLC network modularity was elevated in patients at study entry but normalized over follow-up visits, we performed exploratory analyses to test two competing hypotheses: that network modularity 1) varied with symptom severity (i.e., clinical state); or 2) decreased with greater duration of illness (i.e., clinical stage). Analyses across PANSS subscales (positive, negative, general) revealed that positive symptom severity was selectively associated with modularity values (PANSS-positive, F1,105 = 5.16, p = .03; PANSS-negative and general subscale ps ≥ .25; Figure 3). Follow-up analysis of the PANSS positive subscale showed that higher PANSS-positive symptoms significantly predicted higher modularity (F1,104 = 5.81, p = .02; Figure 3). There was no linear interaction between positive symptoms and time. To determine whether higher PANSS-positive symptoms at study entry drove associations, PANSS-positive scores at study entry were included as a covariate in the linear mixed model. Controlling for the association between modularity and positive symptom severity at study entry, higher positive symptoms significantly predicted higher modularity across visits in patients (PANSS-positive, F1,90 = 4.31, p = .04). This suggests higher modularity at any visit was associated with higher positive symptom severity, regardless of severity at study entry. The association was similar when including duration of illness or HAM-D score as a covariate, and neither covariate independently predicted modularity in patients (duration of illness: F1,106 = 1.03, p = .31; HAM-D: F1,101 = 1.45, p = .23). Examining correlations at individual time points, we found that positive symptom severity was positively correlated with hippocampal-MTLC modularity at study entry and 16-month follow-up (t0: r = .31, p = .02; t1: r = -.02, p = .92; t2: r = .36, p = .04; t3: r = .10, p = .48, Supplementary Table 11). Duration of illness was not correlated with hippocampal-MTLC modularity (ps ≥ .20). Taken together, these findings suggest variation in hippocampal-MTLC connectivity over visits is explained by changes in positive symptom severity, not increasing duration of illness. Hippocampal-MTLC network modularity was also positively correlated with CPZ dose at study entry (t0: r = .30, p = .05), but was not correlated with age, subject education, premorbid IQ, duration of illness, or mood symptoms (ps ≥ .27). Including CPZ dose as a covariate in the model did not alter the association between positive symptoms and modularity.
      Figure thumbnail gr3
      Figure 3(A) Positive symptoms were highest at study entry (t0) and were significantly lower at all subsequent visits (t1-t3). (B) Associations between hippocampal-MTLC modularity, clinical state, and illness duration are visualized across visits. More severe positive symptoms in individual participants were associated with corresponding elevations in modularity of the hippocampal-MTLC network. In contrast, increasing duration of illness over study visits was not associated with changes in hippocampal-MTLC modularity. Dotted lines are 95% confidence intervals. Asterisks indicate significant effects.

      DISCUSSION

      Our goal was to characterize hippocampal network connectivity over two years in the early stage of psychosis. We found that resting-state hippocampal functional connectivity in a core MTL network was strengthened in acute early psychosis patients, with stronger connectivity positively correlated with severity of positive symptoms. In contrast, we did not observe alterations within a broader hippocampal-cortical network. Hippocampal-MTLC network hyperconnectivity normalized longitudinally and was associated with improvement in positive symptoms. Collectively, these results highlight a potential neuroimaging marker for identifying abnormal connectivity in early psychosis patients that relates to clinical symptom severity and clinical improvement, and provides insights into hippocampal network dynamics during the early stages of psychosis.
      Schizophrenia is associated with progressive, but likely non-linear, declines in hippocampal structure across different illness stages (
      • McHugo M.
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      Hippocampal volume in early psychosis: a 2-year longitudinal study.
      ,
      • Ho N.F.
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      • Lieberman J.
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      ,
      • McHugo M.
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      Regionally specific volume deficits along the hippocampal long axis in early and chronic psychosis.
      ,
      • Velakoulis D.
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      • Wong M.T.H.
      • McGorry P.D.
      • Yung A.
      • Phillips L.
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      Hippocampal and amygdala volumes according to psychosis stage and diagnosis: a magnetic resonance imaging study of chronic schizophrenia, first-episode psychosis, and ultra-high-risk individuals.
      ). Hippocampal function may also vary by illness stage (
      • Uhlhaas P.J.
      Dysconnectivity, large-scale networks and neuronal dynamics in schizophrenia.
      ,
      • Li T.
      • Wang Q.
      • Zhang J.
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      • Yang W.
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      Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia.
      ). A primary goal of this study was to determine whether hippocampal network connectivity was disrupted in the early stage of a psychotic disorder. Contrary to our hypothesis of predominantly lowered connectivity, we identified a pattern of elevated hippocampal-MTLC network cohesiveness in early psychosis patients, with the magnitude of elevations similar to that of the visual network. Visual processing deficits (
      • Green M.F.
      • Lee J.
      • Wynn J.K.
      • Mathis K.I.
      Visual masking in schizophrenia: Overview and theoretical implications.
      ) and connectivity abnormalities in early visual processing regions(
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      • Rotarska Jagiela A.
      • Oertel-Knöchel V.
      • Linden D.E.J.
      Reduced intrinsic visual cortical connectivity is associated with impaired perceptual closure in schizophrenia.
      ,
      • Oh K.H.
      • Oh I.S.
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      • Shen J.
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      • Liu C.
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      ,
      • Rolls E.T.
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      ) are prominent in schizophrenia. Hippocampal network elevations are consistent with findings of interneuron hypofunction in patients with schizophrenia (
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      • Heckers S.
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      ,
      • Heckers S.
      • Konradi C.
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      ,
      • Konradi C.
      • Yang C.K.
      • Zimmerman E.I.
      • Lohmann K.M.
      • Gresch P.
      • Pantazopoulos H.
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      ) which could lead to hippocampal hyperactivity (
      • McHugo M.
      • Avery S.
      • Armstrong K.
      • Rogers B.P.
      • Vandekar S.N.
      • Woodward N.D.
      • et al.
      Anterior hippocampal dysfunction in early psychosis: A 2-year follow-up study.
      ), elevated glutamatergic tone (
      • Schobel S.A.
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      • Khan U.A.
      • Paniagua B.
      • Styner M.A.
      • Asllani I.
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      Imaging Patients with Psychosis and a Mouse Model Establishes a Spreading Pattern of Hippocampal Dysfunction and Implicates Glutamate as a Driver.
      ), and sustained hippocampal signaling (
      • Avery S.N.
      • McHugo M.
      • Armstrong K.
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      • Woodward N.D.
      • Heckers S.
      Disrupted Habituation in the Early Stage of Psychosis.
      ,
      • Avery S.N.
      • McHugo M.
      • Armstrong K.
      • JU Blackford
      • Woodward N.D.
      • Heckers S.
      Stable habituation deficits in the early stage of psychosis: a 2-year follow-up study.
      ) in early illness. Given that hyperconnectivity has been associated with psychosis symptoms and cognitive impairments (
      • Driesen N.R.
      • McCarthy G.
      • Bhagwagar Z.
      • Bloch M.
      • Calhoun V.
      • D’Souza D.C.
      • et al.
      Relationship of resting brain hyperconnectivity and schizophrenia-like symptoms produced by the NMDA receptor antagonist ketamine in humans.
      ), abnormally elevated integration of hippocampal-MTLC functional connections is likely indicative of a pathological state arising from disinhibition in the hippocampus.
      Contrary to our hypothesis of widespread deficits, we did not detect connectivity differences across the broader hippocampal-cortical network. One possible explanation is widespread deficits develop in later illness stages as a result of a prolonged period of abnormal hippocampal signaling which entrain abnormal communication within cortical regions (
      • Small S.A.
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      ), culminating in widespread asynchronous communication (
      • Friston K.J.
      The disconnection hypothesis.
      ). Alternatively, it’s possible that medication effects normalize hippocampal-cortical interactions during early illness (
      • Nelson E.A.
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      • Briend F.
      • Armstrong W.
      • Ver Hoef L.W.
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      Anterior Hippocampal-Cortical Functional Connectivity Distinguishes Antipsychotic Naïve First-Episode Psychosis Patients from Controls and May Predict Response to Second-Generation Antipsychotic Treatment.
      ,
      • Chopra S.
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      • O’Donoghue B.
      • Sabaroedin K.
      • Arnatkeviciute A.
      • Cropley V.
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      ,
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      ). As the vast majority of patients in this study were taking antipsychotic medication during one or more study visit (94%), we were unable to test whether hippocampal-cortical modularity values differed in those not exposed to antipsychotics, although modularity values were not associated with antipsychotic dose. Importantly, connectivity within the MTL may not be normalized even with antipsychotic treatment (
      • Chopra S.
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      • Cropley V.
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      ), suggesting hippocampal-MTLC connectivity may represent a core illness-related feature of psychosis.
      The second key goal of the study was to determine whether hippocampal network connectivity changes over two years of follow-up. Longitudinal analyses revealed three key findings: 1) hippocampal-MTLC hyperconnectivity normalized in our longitudinal sample, with connectivity in early psychosis patients resembling that of healthy control participants at two-year follow-up; 2) connectivity was not associated with duration of illness at study entry and normalization was not associated with progression of illness; and 3) connectivity normalization was correlated with improvement in psychosis symptoms over longitudinal follow-up. Together, these findings suggest hippocampal-MTLC network hyperconnectivity may be a marker of current clinical state. Alterations in hippocampal function and connectivity have been proposed as a foundational mechanism in the formation of psychotic symptoms (
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      ). Abnormally elevated resting activity in the hippocampus and MTLC, particularly parahippocampal cortex, has been associated with the presence or severity of positive symptoms in schizophrenia by a number of studies (
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      We need to consider several limitations to our study. First, given the drawbacks of data-driven approaches in delineating the hippocampal network, we used an anatomically-guided approach. Cortical nodes were comprised of regions that showed functional connectivity with the parahippocampal and perirhinal cortex in an independent sample (
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      ). Finally, as the majority of patients were taking antipsychotic medications during the study, alterations in connectivity related to disease state and medication use are intrinsically linked. As antipsychotic use affects hippocampal connectivity (
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      ), additional longitudinal studies of hippocampal network function in medication-naïve patients are warranted.
      In conclusion, this study established functional connectivity was elevated within a core hippocampal-MTLC network in early psychosis and associated with the severity of positive symptoms. Hippocampal hyperconnectivity normalized longitudinally and predicted clinical improvement. These results suggest hippocampal-MTLC hyperconnectivity may be a useful biomarker development target (
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      ). Longer-term prospective studies are necessary to determine whether additional hippocampal connectivity changes occur during progression to chronic stages of psychosis.

      ACKNOWLEDGEMENTS

      This work was supported by the Charlotte and Donald Test Fund, NIMH grant R01-MH70560 (Dr. Heckers), R01-MH123563 (Dr. Vandekar), the Vanderbilt Psychiatric Genotype/Phenotype Project, and the Vanderbilt Institute for Clinical and Translational Research (through grant 1-UL-1-TR000445 from the National Center for Research Resources/NIH). This work was conducted in part using the resources of the Center for Computational Imaging at Vanderbilt University Institute of Imaging Science and the Advanced Computing Center for Research Education at Vanderbilt University, Nashville, TN.

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