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Background: 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental syndrome associated with deficits in cognitive and emotional processing. This syndrome represents one of the highest risk factors for the development of schizophrenia. Previous studies of functional connectivity (FC) in 22q11DS report aberrant connectivity patterns in large-scale networks that are associated with the development of psychotic symptoms. Methods: In this study, we performed a functional connectivity analysis using the CONN toolbox to test for dif- ferential connectivity patterns between 54 individuals with 22q11DS and 30 healthy controls, between the ages of 17–25 years old. We mapped resting-state fMRI data onto 68 atlas-based regions of interest (ROIs) generated by the Desikan-Killany atlas in FreeSurfer, resulting in 2278 ROI-to-ROI connections for which we determined total linear temporal associations between each. Within the group with 22q11DS only, we further tested the association between prodromal symptoms of psychosis and FC. Results: We observed that relative to controls, individuals with 22q11DS displayed increased FC in lobar networks involving the frontal–frontal, frontal–parietal, and frontal–occipital ROIs. In contrast, FC between ROIs in the pari- etal–temporal and occipital lobes was reduced in the 22q11DS group relative to healthy controls. Moreover, positive psychotic symptoms were positively associated with increased functional connections between the left precuneus and right superior frontal gyrus, as well as reduced functional connectivity between the bilateral pericalcarine. Positive symptoms were negatively associated with increased functional connectivity between the right pericalcarine and right postcentral gyrus. Conclusions: Our results suggest that functional organization may be altered in 22q11DS, leading to disruption in connectivity between frontal and other lobar substructures, and potentially increasing risk for prodromal psychosis. Keywords: Functional connectivity, Connectives, Frontal lobe dysconnectivity, Velo-cardio-facial syndrome, 22q11.2 deletion syndrome, Schizophrenia Background with the syndrome typically present with physical anom- Chromosome 22q11.2 deletion syndrome (22q11DS) is alies, cognitive impairments, and behavioral disorders [1, caused by a microdeletion of approximately 50 genes on 2]. During adolescence and young adulthood, approxi- one copy of the q11.2 band of chromosome 22. Youth mately 30–40% of individuals with 22q11DS develop a psychotic illness, usually schizophrenia [3–5]. This repre - sents a significant increase over the risk for schizophrenia *Correspondence: email@example.com in the general population . The neurobiological mech - Department of Psychiatry and Behavioral Sciences, State University anisms underlying this increased risk for schizophrenia of New York Upstate Medical University, 750 East Adams Street, Syracuse, in individuals with 22q11DS are not well-understood. NY, USA Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 2 of 11 Converging evidence supports the notion that idi- the brain. Conceptually, a subject-specific, atlas-based opathic (non-syndromal) schizophrenia is a disorder of approach can yield additional data about the functional functional and structural dysconnectivity [7–11]. Stud- architecture and organization of the brain [28, 29]. More- ies of functional connectivity point to a preponderance over, the use of atlas-based ROIs provides a common of anomalies in frontal–temporal connectivity [12, 13], framework to increase reproducibility across studies, and although frontal–parietal and frontal–occipital con- can be incorporated for use in multimodal studies. In nections have also been implicated [14, 15]. Moreover, order to implement this approach, we applied the func- abnormalities have been observed in several large-scale, tional connectivity toolbox, CONN [28–30], which has functional networks, including the default mode net- shown a high degree of interscan reliability  and has work, the salience network and the central executive net- demonstrated disease-relevant functional connections work [16–18]. between anatomically defined regions of the brain . Although studies examining functional dysconnectivity We hypothesized that ROI-to-ROI connectivity between in 22q11DS are much fewer in number, the findings are sublobar frontal–parietal gyri, and frontal–temporal gyri consistent with studies of idiopathic schizophrenia . would be anomalous in individuals with 22q11DS rela- Results of these studies indicate anomalous connectiv- tive to controls, and that aberrant connectivity would be ity in frontal lobe connections  and parieto–occipital associated with symptoms of psychosis. connections [20–22]. Decreases in functional connec- tivity have also been observed, in partially overlapping Methods samples, in the default mode [23–26], salience  and Participants frontal–parietal networks [22, 24]. In a modularity analy- Data were acquired from a large-scale longitudinal study sis of overall functional network organization, Scariati of risk factors for psychosis in 22q11DS conducted at and colleagues  observed increased modular segre- SUNY Upstate Medical University, Syracuse, NY. Our gation across superior parietal, frontal and inferior tem- sample consisted of 84 participants: 54 with 22q11DS poral lobes in individuals with 22q11DS. Associations (30 males; mean age 20.98, SD 2.35) and 30 controls (16 between anomalous functional connectivity in 22q11DS males; mean age 20.97, SD 1.46). The control sample con - and increased symptoms of psychosis have been observed sisted of 12 healthy siblings of individuals with 22q11DS, in most [20, 22, 24], but not all studies . and 18 community controls. Since siblings and commu- To our knowledge, two studies by Scariati and col- nity controls did not differ in either demographic vari - leagues [20, 27] have conducted a functional connectivity ables or measures of functional connectivity (Additional analysis of atlas-based, ROI-to-ROI structural connec- file 1), they were combined into one control group. A tions in 22q11DS. Scariati and colleagues first reported previous publication included 39 of the 54 (72.2%) partic- widespread functional connectivity in individuals with ipants with 22q11DS in the current report, which tested 22q11DS, primarily affecting frontal and temporal lobe differential connectivity in resting-state networks utiliz - regions. In a more recent study , they focused on ing independent component analysis and associations age differences by examining connectivity in a sample with psychiatric and neurocognitive functioning . of 9–30 year-old individuals with 22q11DS that were Additionally, a recent publication including a partially divided into two age groups (groups split at 18 years old) overlapping sample of the 22q11DS group in this report for subanalyses. In both age groups, alterations of mod- demonstrated hypoconnectivity as a classifier in the iden - ular communities were found to affect the anterior cin - tification of 22q11DS versus control groups . gulate cortex and parieto-occipital processing regions. Diagnosis of 22q11DS was confirmed by fluorescence However, in adults with 22q11DS, they observed non- in situ hybridization (FISH). Recruitment details have typical modularity partition of the dorsolateral prefrontal been described previously . Briefly, exclusion cri - cortex. teria included seizure disorder, fetal exposure to alco- Here, we conduct an atlas-based functional connec- hol or drugs, parent-reported elevated lead levels or tivity analysis of ROI-to-ROI connections in individuals birth weight under 2500 g, loss of consciousness lasting with 22q11DS who are specifically between the ages of 18 longer than 15 min, paramagnetic implants, or ortho- and 24 years, a time-frame that poses the greatest risk for dontic braces. Potential controls with a personal or fam- developing psychotic illness. In this ROI-to-ROI based ily history of schizophrenia or bipolar disorder were also approach, we sought to assess connectivity patterns by excluded . Since data for the current report were matching an anatomical atlas to each subject’s own fMRI taken from a longitudinal study, control participants who space. The methodological advantage of this approach had presented with an anxiety disorder and/or depression is that data were not normalized to a standard template, at the first timepoint were excluded. However, the cur - thus obviating potentially problematic effects of warping rent report depicts data from the last (fourth) timepoint, Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 3 of 11 and controls that subsequently developed an anxiety Table 2 Demographic data for prodromal and nonprodro- mal subgroups disorder or depression in the longitudinal study were included. Controls with ADHD or a learning disability Prodromal Overt Nonpro- p value were not excluded at any timepoint in the study to maxi- N = 10 N = 5 dromal N = 39 mize comparability to higher functioning participants in the 22q11DS group. Of the 54 participants, 22 were a Age 22.60 (2.50) 19.43 (1.54) 20.76 (2.21) 0.320 being treated with one or more antidepressant, antianxi- Gender (male, %) 5 (50.0%) 2 (40.0%) 23 (58.97%) 0.436 ety, antipsychotic, or stimulant medications at the time of a Full scale IQ 71.0 (6.65) 61.6 (4.62) 76.92 (12.53) 0.002 their scan. Three controls were being treated with either Demographic and psychiatric data for prodromal, nonprodromal, and a stimulant and/or antidepressant/antianxiety medica- participants with overt psychosis from our initial sample of 55; 1 proband was tion. Details of the samples can be found in Table 1. excluded due to image quality Within the 22q11DS group, 10 participants were cur- Mean and standard deviation are provided for age and full scale IQ. Independent t tests were conducted to determine differences between rently experiencing positive prodromal symptoms of psy- prodromal and nonprodromal subgroups; participants with overt psychosis chosis (based on a frequency of symptoms > 1 week, and were combined with the prodromal group for subsequent analyses a score of equal or greater than 3 on the positive symp- toms subscale of the Structured Interview for Prodro- resulting in an interclass correlation coefficient of 0.91. mal Symptoms [SIPS; ]). An additional 5 participants The presence of prodromal, positive symptoms of psy - were diagnosed with overt psychosis. Additional details chosis was determined utilizing the Structured Interview regarding these subgroups can be found in Table 2. The for Prodromal Syndromes (SIPS; ), conducted within institutional review board of SUNY Upstate Medical Uni- the context of the psychiatric evaluation. Additional versity approved all study procedures, and each partici- details regarding psychiatric diagnoses can be found in pant provided written informed consent or assent. Table 1. Psychiatric assessment Image acquisition Participants had psychiatric evaluations administered by Both anatomical and functional resting-state imaging two doctoral-level clinicians (WF and KMA). To deter- data were acquired with a Siemens Tim Trio, 3 Tesla mine the presence of DSM-IV psychiatric diagnoses scanner with an 8-channel head coil receiver (Siemens in both the 22q11DS and control group, the Structured Medical Solutions, Erlangen, Germany) during the same Clinical Interview for DSM-IV Axis I disorders (SCID; scanning session. T1-weighted images were acquired in ) was administered. Inter-rater reliability was calcu- the sagittal plane utilizing a MPRAGE pulse sequence lated based on 5 consecutive, audio-recorded interviews with the following parameters: TR/TE = 2530/3.31 ms, voxel size = 1.0 × 1.0 × 1.0, flip angle = 7 , field of Table 1 Demographic and psychiatric data view = 256 mm, and 256 × 256 acquisition matrix. Blood oxygen level dependent (BOLD) images were acquired 22q11DS Controls p value during a 5-minute resting-state scan, which included 152 N = 54 N = 30 images (34 axial slices, 4 mm thickness, no gap) utiliz- Age 20.98 (2.35) 20.97 (1.46) 0.990 ing an ep2d_bold sequence: TR/TE = 2000/30 ms, voxel Gender (male, %) 30 (55.6%) 16 (53.3%) 0.847 size 4.0 × 4.0 × 4.0, flip angle = 90 , field of view = 256, Full scale IQ 74.41 (12.0) 109.47 (16.02) < 0.001 acquisition matrix = 64 × 64. Participants were Psychiatric diagnosis, n (%) instructed to keep their eyes open and not to fall asleep Psychotic disorder 5 (9.26%) 0 (0%) 0.024 during the scanning session. ADHD 8 (14.81%) 5 (16.67%) 0.825 Anxiety disorder 11 (20.37%) 4 (13.33%) 0.426 Image processing Mood disorder 7 (12.96%) 1 (6.25%) 0.094 Raw structural data were imported into the FreeSurfer Current medication, n (%) image analysis suite (v5.1.0, https://surfer.nmr.mgh.har- Antipsychotic/mood 8 (14.81%) 0 (0%) 0.004 stabilizer vard.edu/ ) for removal of non-brain tissue. The gen - Antidepressant/anti- 16 (29.63%) 2 (6.67%) 0.004 erated brain mask was then manually edited in 3DSlicer anxiety 4 (https://www.slicer.org/ ). Edited brain masks were Stimulant 9 (16.67%) 2 (6.67%) 0.151 then aligned in 3DSlicer along the anterior and poste- Demographic and psychiatric data for participants in our group analyses; from rior commissure using a cubic spline transformation. our initial sample of 85, one proband was excluded due to image quality Resolution was maintained at 1 mm cubic isotropic vox- Mean and standard deviation are provided for age and full scale IQ. els. Preprocessed data were then introduced into Free- Independent t tests were conducted to determine differences between 22q11DS and control samples Surfer’s automated surface-based reconstruction and Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 4 of 11 volume-based subcortical processing streams to seg- connectivity map. A bivariate correlation was used to ment, and parcellate the brain into 68 regions based on determine total linear temporal associations between the Desikan-Killiany atlas . To briefly summarize, each of the resulting 2278 ROI-to-ROI functional con- this processing pipeline includes motion correction, nections. Second-level analyses of group differences in intensity normalization, registration to Talairach space, functional connectivity between 22q11DS and controls removal of non-brain matter, cortical reconstruction, and was conducted through the CONN toolbox and FDR- segmentation of subcortical structures and white mat- corrected, p < 0.05, two-tailed. ter. Before final reconstruction was run, manual inter - We then repeated the aforementioned ROI-to-ROI vention using control points were placed to minimize analysis to compare functional connectivity between pro- motion and hyperintensities that were not corrected by dromal and nonprodromal participants with 22q11DS the automated pipeline. Details of manual intervention based on positive symptoms that were present at a fre- protocols can be found in McCarthy and colleagues . quency of greater than once per week, and that obtained Second reconstruction was then conducted considering summed scores of ≥ 3 (reflecting intensity of the symp - any manual intervention. Final reconstruction steps were tom) on the Structured Interview for Prodromal Symp- then run to complete the processing pipeline. toms (SIPS; ) positive symptoms subscale. These Functional data were preprocessed using statistical criteria have been applied in previous studies of individu- parametric mapping (SPM5; Wellcome Trust Centre for als with 22q11DS [20, 24]. Neuroimaging, 2005, London, UK, http://www.fil.ion.ucl. ac.uk/spm/ ). Images were visually inspected for the Associations with positive symptoms presence of significant signal dropout, ghosting, excessive We then tested associations between positive symptom noise, and any other artifact that would impact the abil- scores in 22q11DS (taken from summed scores of the ity to analyze the images. Visual inspection was repeated SIPS Positive Symptoms subscale) and functional con- throughout different stages of preprocessing. Images nectivity values for ROI-to-ROI connections that were were first motion corrected using INRIalign , an algo - significantly different between individuals with 22q11DS rithm that is unbiased by local signal changes. Motion and the control group. Functional connectivity values adjustment, an algorithm that suppresses residual fluctu - were taken from Fisher-transformed correlation coef- ations due to errors in interpolation from large motions ficients from the first-level analysis conducted in the was subsequently conducted using ArtRepair . A CONN toolbox. Since many participants with 22q11DS despiking function was then applied to remove any spikes scored 0 on the SIPS Positive Symptoms Scale (29 par- caused by motion. No participants were excluded due to ticipants, 53.7%), and since the SIPS produces a count motion based on the following criteria: > 2 mm across the variable, we conducted a zero-inflated Poisson (ZIP) entire run and rotation greater than 2°. One proband was regression analysis to examine these associations. Results excluded due to a significant signal dropout in the raw were then FDR-corrected, p < 0.05. BOLD images, and no other participants were excluded for any other artifacts mentioned above. Results Anatomical T1-weighted images from FreeSurfer, Second-level analyses of the functional connectome anal- (including each ROI for both hemispheres) were then ysis revealed significant differences in functional con - coregistered to the mean functional EPI image in SPM nectivity between 22q11DS and controls (p < 0.05). FDR for each participant. (Table 3 and Fig. 1) At the lobar level, we observed differ - ential connectivity between ROIs within frontal–frontal, Functional connectivity analysis frontal–occipital, frontal–parietal, occipital–occipital, Functional connectivity analyses were conducted utiliz- and parietal–temporal regions. ing the CONN toolbox (https://www.nitrc.org/projects/ conn ). This toolbox implements a CompCor method, Increased functional connectivity in 22Q11DS vs. controls which reduces physiological and movement effects: CSF Within frontal–frontal connections, we observed increased and white matter effects, task-related effects, and rea - functional connectivity in individuals with 22q11DS rela- lignment parameter noise without removing the global tive to controls between the right precentral gyrus and signal . A band-pass filter of 0.008–0.09 was applied right posterior cingulate, right superior frontal gyrus to left to the data. Realignment parameters from preprocess- posterior cingulate, and right superior frontal gyrus to right ing were entered as confounds in the first-level analysis. posterior cingulate. Table 3 displays differential functional Using the Desikan-Killany atlas in FreeSurfer , which connections between 22q11DS and controls at both the generates 34 bilateral, or 68 ROI’s, we conducted a seed- lobar and sublobar level as well as t values, corrected p val- based ROI-to-ROI analysis to create a 68 × 68 functional ues, and averaged functional connectivity values. Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 5 of 11 Table 3 Differential functional connectivity between 22q11DS and controls a a Functional connection (ROI–ROI) Lobar-level connections t value p value, corr 22q11DS controls 22q11DS vs controls Right precentral–right posterior cingulate Frontal–frontal 3.59 0.038 0.232 0.067 Right superior frontal–left posterior cingulate Frontal–frontal 3.22 0.025 0.230 0.036 Right superior frontal–right posterior cingulate Frontal–frontal 3.23 0.025 0.411 0.212 Right pars orbitalis–left cuneus Frontal–occipital 3.79 0.019 0.011 − 0.187 Right pars orbitalis––right cuneus Frontal–occipital 3.44 0.022 0.021 − 0.146 Right pericalcarine–left paracentral Frontal–occipital 3.42 0.033 − 0.013 − 0.173 Right pericalcarine–right postcentral Frontal–occipital 3.27 0.035 − 0.013 − 0.159 Right precuneus–right caudal middle frontal Frontal–parietal 4.04 0.008 0.281 0.054 Left Precuneus–right pars orbitalis Frontal–parietal 3.42 0.033 − 0.109 − 0.313 Right precuneus–right pars orbitalis Frontal–parietal 3.23 0.04 0.014 − 0.174 Left precuneus–right superior frontal Frontal–parietal 4.06 0.008 0.110 − 0.113 Right precuneus–right superior frontal Frontal–parietal 3.30 0.04 0.289 0.092 Right superior frontal–right lateral orbito frontal gyrus Frontal–frontal − 3.37 0.025 0.102 0.312 Right pericalcarine–left pericalcarine Occipital–occipital − 3.98 0.01 1.254 1.488 Left superior parietal–left fusiform Parietal–temporal − 3.55 0.021 0.208 0.382 Left superior parietal–left inferior temporal gyrus Parietal–temporal − 3.63 0.021 0.156 0.379 Functional connections displayed within this table represent connections that were significantly different between 22q11DS and controls, FDR-corrected, p < 0.05 Mean functional connectivity values reported for each study group Increased functional connectivity was also observed Associations with psychosis in 22q11DS in frontal–occipital connections: between the right pars After correction for multiple comparisons, (p < 0.05) FDR orbitalis and left cuneus, right pars orbitalis and right a ZIP regression analysis reported increased func- cuneus, right pericalcarine and left paracentral gyri, and tional connectivity between the left precuneus and right right pericalcarine and right postcentral gyri. Relative to superior frontal was positively associated with positive controls, increased functional connectivity was again dis- symptoms (z = 5.72, p = 0.008). Reduced functional con- played within frontal-parietal connections: between the nectivity between the right pericalcarine and left perical- right precuneus to the right caudal middle frontal gyrus, carine was positively associated with positive symptoms left precuneus and right pars orbitalis, right precuneus (z = 4.39, p = 0.008). Increased functional connectivity and right pars orbitalis, left precuneus and right superior between the right pericalcarine and right postcentral frontal gyrus, right precuneus and right superior frontal were found to be negatively associated with positive psy- gyrus. chotic symptoms (z = − 2.95, p = 0.016) (see Fig. 3). Reduced functional connectivity in 22Q11DS vs. controls Heterogeneity effects in controls Reduced functional connectivity was observed between Since seven of our controls in the current report were the right superior frontal gyrus and right lateral orbito- diagnosed with an anxiety disorder, depression, or frontal cortex. We also observed reduced functional con- ADHD, we conducted a separate functional connectivity nectivity in 22q11DS in parietal-temporal connections: analysis in CONN excluding those seven participants to between the left superior parietal lobule and left fusiform account for any potential confounding effects in our FC results. Our findings remained significant after FDR cor gyrus and left superior parietal lobule and left inferior - temporal lobe. rection, p < 0.05, and we continued to observe the same patterns of increased/decreased functional connectivity Functional connectivity within 22Q11DS between the frontal–occipital, frontal–parietal, occipi- Between the nonprodromal and prodromal 22q11DS tal–occipital, and superior parietal-inferior temporal groups, we observed only one significant difference connections. However, we did observe that once these between groups: increased functional connectivity controls were excluded, functional connectivity between between the left inferior temporal and right pericalcarine frontal–frontal regions (superior frontal lobe–posterior gyri (t = 3.68, p = 0.038) (Fig. 2). cingulum; precentral gyrus–posterior cingulum) and one FDR Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 6 of 11 Fig. 1 This figure depicts significant differences in functional connectivity between 22q11DS and control samples. The color bar represents t values of results in axial (top) and left and right sagittal views. Red indicates increased FC in 22q11DS and blue indicates reduced FC in 22q11DS frontal–parietal connection (pars orbitalis–precuneus) of the default mode network (DMN), which as noted no longer met threshold for significance. above, is reported to be anomalous in both schizophrenia and 22q11DS. Studies have demonstrated that the DMN Discussion is active not only during rest but also during activities Using a seed-based connectivity analysis of 2278 ROI- involving self-referential  and social-interpersonal to-ROI connections, we observed both hyper- and hypo- processing . Evidence suggests that the DMN may connectivity in frontal–frontal gyri, frontal–parietal gyri, be involved in auditory hallucinations in individuals frontal–occipital gyri, parietal–temporal gyri and occipi- with schizophrenia [43–45], although other networks tal–occipital gyri in young adults with 22q11DS rela- have been implicated as well [46, 47]. In individuals with tive to controls. Notable findings included (1) increased 22q11DS, the DMN has been associated with prodromal functional connectivity between frontal (superior fron- symptoms , sustained attention  and recipro- tal, caudal middle frontal and pars orbitalis) gyri and cal social behaviors . It is not clear why we observed the precuneus, and (2) increased functional connectiv- increased functional connectivity between these DMN ity between posterior cingulate gyrus and both superior regions, while several other studies [23–26] of 22q11DS frontal and precentral gyri. Anomalies in frontal–parietal have observed decreased functional connectivity and occipital–occipital gyral connectivity were signifi - between these regions. This may be attributable, in part, cantly associated with positive symptoms of psychosis. to our implementation of measurements within each The precuneus, caudal middle frontal and pars orbit - subject’s native brain space. In light of the anatomic dif- alis (i.e., medial inferior frontal) regions constitute part ferences that have been reported in brains of individuals Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 7 of 11 Fig. 2 This figure depicts differential functional connectivity between prodromal and nonprodromal (prodromal > nonprodromal) 22q11DS sam- ples represented by left sagittal and superior axial views Fig. 3 This figure depicts plots representing associations between total positive symptoms scores measured by the SIPS and functional connectiv- ity in connections that were significantly different between 22q11DS and controls with 22q11Ds, retaining each subject’s native brain space and colleagues ). Additional insight into why our find - may have produced results that are not totally (anatomi- ing of increased functional connectivity in the DMN dif- cally) comparable to studies in which brains are warped fers from several (but not all [21, 22]) studies of 22q11DS to a standard template. Moreover, potential differences in is suggested by the results of two previously-published sample characteristics (e.g. IQ levels; medication usage) papers [22, 24] that included samples that overlapped between studies may also be contributing to differences with the sample of the current. In our two previously- in the direction of these results (see review by Scariati published papers, we pooled samples from two research Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 8 of 11 sites, and applied Independent Components Analyses impairments that are associated with 22q11DS [20, 23]. to the pooled data. However, preprocessing methods For example, in addition to schizophrenia, frontal dys- differed somewhat between the two papers. In the first connectivity has been implicated in both autism spec- paper, by Mattiaccio and colleagues , for which data trum disorders and in ADHD, both of which are elevated were preprocessed and analyzed at our site, increased in 22q11DS [5, 57, 64–68]. functional connectivity in the DMN was observed. In In our sample, positive prodromal symptoms of psy- the second paper, by Schreiner and colleagues , the chosis were associated with increased connectivity data were preprocessed and analyzed by our collaborat- between the superior frontal gyrus and the precuneus, ing site, and decreases in functional connectivity in the and with decreased connectivity between the right and DMN were observed. Interestingly, our respective sites’ left pericalcarine gyri of the occipital lobe, and between preprocessing methods differed in motion correction and pericalcarine and postcentral gyri. As noted above, the noise reduction strategies, potentially accounting for the precuneus and aspects of the superior frontal gyrus are discrepancies in results. This supports the notion that included in the DMN, which previous studies of 22q11DS differences in image processing methods and in sample have associated prodromal symptoms as well . Asso- characteristics may be contributing to between-study dif- ciations between parietal–occipital and occipital–occipi- ferences in results. tal functional connections and prodromal symptoms of The posterior cingulate gyrus (PCG) is also part of the psychosis have not been reported. However, anatomic default mode network, and we found anomalies in con- connections between parietal and occipital lobes, via nectivity between PCG and superior frontal and pre- the superior longitudinal fasciculus (SLF), have been central gyri. The extent to which PCG—superior frontal reported to be aberrant in 22q11DS [69–72]. Moreo- connections in our study reflects the DMN is not com - ver, in an overlapping sample, our group  recently pletely clear, since we utilized a predefined, atlas-based reported associations between anatomic anomalies in the approach that maps onto regions that subsume, but are SLF and prodromal symptoms. not synonymous with the DMN. Nonetheless, primate When we divided the group of individuals with (and more recently, human imaging) studies indicate 22q11DS into prodromal and nonprodromal subgroups, that the PCG has strong, reciprocal connections to the we observed a significant difference in connectivity dorsolateral prefrontal cortex (DLPFC) [48–50], which between the left inferior temporal and right pericalcar- overlaps with the superior frontal region included in the ine gyri. Interestingly, we recently reported (in the same Desikan-Killany atlas. It has been suggested that PCG- patient sample) significant associations between white DLPFC connections may be part of both the dorsal atten- matter microstructural anomalies in the temporal-occip- tion network and the frontal-parietal control network ital aspect of the inferior longitudinal fasciculus and  both of which contribute to efficient cognitive func - symptoms of psychosis . Temporal-occipital altera- tion. Functional connectivity of the PCG and the superior tions in functional connectivity have also been reported aspect of the DLPFC has been linked to goal-directed in patients experiencing their first episode of psychosis thought processes , suggesting that this reciprocal , further supporting the validity of these observations. connection may subserve executive planning [53, 54] and cognitive control [53, 55], both of which are impaired in Limitations and conclusions individuals with 22q11DS [56–59]. Moreover, these func- Our study utilized an atlas-based approach to investi- tional brain networks have been shown to be impaired in gate functional connectivity in 22q11DS, which permit- schizophrenia [14, 60, 61] and 22q11DS [22, 24, 62]. ted us to examine, within each individual’s own fMRI Of the 16 ROI-to-ROI connections that significantly space, more than 2000 functional connections through- differentiated individuals with 22q11DS from controls, out the cortex. A potential limitation to our method is 13 (81%) of them included at least one ROI in the frontal that the acquisition time of 5 min that we used to acquire lobe. These findings are consistent with other functional our fMRI data, while minimally acceptable for an fcMRI connectivity studies of both idiopathic schizophrenia [7, study, may not be optimal in order to minimize the 12, 13, 63] and 22q11DS [20, 23] and suggest that both effects of noise and ensure the detection of small correla - short-range and long-range connectivity of the fron- tions that might otherwise go unobserved . A second tal lobe is anomalous in individuals with this syndrome. potential limitation is that the connections we examined To the extent that the frontal lobe subserves a myriad of do not necessarily map specifically onto the networks cognitive and social-affective functions, functional dys - that are traditionally examined in resting state fcMRI connectivity of networks that include the frontal lobe studies, thus limiting comparisons to other studies to could underlie many of the cognitive and psychiatric some extent, and rendering conclusions regarding these Mattiaccio et al. Behav Brain Funct (2018) 14:2 Page 9 of 11 Author details comparisons somewhat speculative. Nonetheless, our Department of Psychiatry and Behavioral Sciences, State University of New results concur generally with previous studies that have York Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. observed DMN anomalies in 22q11DS and associations Department of Computer Science, State University of New York at Oswego, Oswego, NY, USA. Department of Psychology, Syracuse University, Syracuse, between DMN anomalies and prodromal symptoms of NY 13210, USA. psychosis. However, we observed increased functional connectivity in DMN regions, in contrast to several pre- Acknowledgements Not applicable. vious studies that have observed reduced connectivity. As noted above, this may be due in part to the potential Competing interests impact of current medication usage in our sample, and to The authors declare that they have no competing interests. study differences in image preprocessing. In addition, it Availability of data and materials should be noted that when we removed the subset of con- The datasets used and/or analyzed during the current study are available from trols with ADHD and anxiety, study group differences in the corresponding author upon reasonable request. the connections between the PCG and both the superior Consent for publication frontal and precentral gyri did not survive correction for Not applicable. multiple comparisons. This may suggest that the presence Ethics approval and consent to participate of psychiatric disorders in our sample may be influencing The Institutional Review Board of SUNY Upstate Medical University approved our observation of study group differences in connectiv - all study procedures, and each participant provided written informed consent ity between PCG and other frontal-based regions; how- or assent. ever, the removal of the control subgroup also reduced Funding power to detect differences. Accordingly, future stud - Support for this study was provided by the National Institutes of Health, ies would benefit from larger samples to elucidate the MH064824, to Wendy R. Kates. potential interplay between the presence of psychiatric disorders in 22q11DS and functional connectivity. To the Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- extent that sampling and image preprocessing differences lished maps and institutional affiliations. account for discrepancies across studies, it would be use- ful, in general, to apply different preprocessing methods Received: 31 July 2017 Accepted: 4 January 2018 to identical samples in order to elucidate the extent to which these methods account for differences in results of functional connectivity studies. Within the area of neu- rofunction in 22q11DS, future studies should examine References the associations between functional and structural con- 1. 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Behavioral and Brain Functions – Springer Journals
Published: Dec 1, 2018
Keywords: neurosciences; neurology; behavioral therapy; psychiatry
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