Gender Differences in the Expression of Verbal Memory Deficits in Unaffected Parents of Patients With Schizophrenia

Pentaraki, Alexandra; Stefanis, Nicholas C.; Toulopoulou, Timothea; Ståhl, Daniel; Kaliora, Styliani; Roukas, Dimitrios; Theleritis, Christos; Chatzimanolis, I.; Smyrnis, N.; Russell, Tiffany D.; Kravariti, Eugenia; Murray, Ruth

doi:10.1016/s0920-9964(08)70573-5

Cited by 2 publications

(5 citation statements)

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“…Specifically, pseudoneglect refers to a leftward attentional bias that occurs in normally functioning individuals and causes them to bisect visually presented lines slightly to the left of their true center (e.g., McCourt, 2001; Foxe et al, 2003; McCourt et al, 2008). This spatial bias has been attributed to the well-known right hemisphere specialization for spatial attention (see e.g., Mesulam, 2000), and indeed, both functional imaging (Fink et al, 2001 and ERP studies (Foxe et al, 2003) show that visuospatial judgments during line-bisection are subserved by right parietal regions.…”

Section: Discussionmentioning

confidence: 99%

“What” and “Where” in Auditory Sensory Processing: A High-Density Electrical Mapping Study of Distinct Neural Processes Underlying Sound Object Recognition and Sound Localization

Leavitt¹,

Molholm²,

Gomez‐Ramirez³

et al. 2011

Front. Integr. Neurosci.

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Functionally distinct dorsal and ventral auditory pathways for sound localization (WHERE) and sound object recognition (WHAT) have been described in non-human primates. A handful of studies have explored differential processing within these streams in humans, with highly inconsistent findings. Stimuli employed have included simple tones, noise bursts, and speech sounds, with simulated left–right spatial manipulations, and in some cases participants were not required to actively discriminate the stimuli. Our contention is that these paradigms were not well suited to dissociating processing within the two streams. Our aim here was to determine how early in processing we could find evidence for dissociable pathways using better titrated WHAT and WHERE task conditions. The use of more compelling tasks should allow us to amplify differential processing within the dorsal and ventral pathways. We employed high-density electrical mapping using a relatively large and environmentally realistic stimulus set (seven animal calls) delivered from seven free-field spatial locations; with stimulus configuration identical across the “WHERE” and “WHAT” tasks. Topographic analysis revealed distinct dorsal and ventral auditory processing networks during the WHERE and WHAT tasks with the earliest point of divergence seen during the N1 component of the auditory evoked response, beginning at approximately 100 ms. While this difference occurred during the N1 timeframe, it was not a simple modulation of N1 amplitude as it displayed a wholly different topographic distribution to that of the N1. Global dissimilarity measures using topographic modulation analysis confirmed that this difference between tasks was driven by a shift in the underlying generator configuration. Minimum-norm source reconstruction revealed distinct activations that corresponded well with activity within putative dorsal and ventral auditory structures.

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Section: Discussionmentioning

confidence: 99%

“What” and “Where” in Auditory Sensory Processing: A High-Density Electrical Mapping Study of Distinct Neural Processes Underlying Sound Object Recognition and Sound Localization

Leavitt¹,

Molholm²,

Gomez‐Ramirez³

et al. 2011

Front. Integr. Neurosci.

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“…Interestingly, the HCs and H-FDRs were similar in terms of the informative FNs, while the SCZs and the S-FDRs were less similar to the other groups, suggesting that the SCZs and S-FDRs were more heterogeneous in terms of the FNs than the HCs and H-FDRs. However, most previous studies of unaffected FDRs or high-risk relatives of SCZ patients typically considered them as a homogenous group (Dodell-Feder et al, 2014;Guo et al, 2014;Meda et al, 2008;van Buuren et al, 2012;Woodward et al, 2007) or categorized them by demographic and clinical characteristics (Bhojraj et al, 2011;Choi et al, 2008;Pentaraki et al, 2008). This may lead to limited sensitivity to capture subtle differences between FDRs and HCs or patients.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies of large‐scale intrinsic functional networks (FNs) have also demonstrated that individuals at high risk for psychosis and SCZ patients share disease‐specific patterns (Collin, Kahn, de Reus, Cahn, & van den Heuvel, ; Guo et al, ; Repovs, Csernansky, & Barch, ; van Buuren, Vink, & Kahn, ). However, unaffected FDRs are typically considered to be a homogeneous group (Dodell‐Feder et al, ; Guo et al, ; Meda et al, ; van Buuren et al, ; Woodward, Tibbo, & Purdon, ) or are categorized by demographic and clinical characteristics (Bhojraj, Francis, Montrose, & Keshavan, ; Choi et al, ; Pentaraki et al, ), which may have limited sensitivity to capture subtle differences between FDRs and healthy controls (HCs).…”

Section: Introductionmentioning

confidence: 99%

“…However, unaffected FDRs are typically considered to be a homogeneous group (Dodell-Feder et al, 2014;Guo et al, 2014;Meda et al, 2008;van Buuren et al, 2012;Woodward, Tibbo, & Purdon, 2007) or are categorized by demographic and clinical characteristics (Bhojraj, Francis, Montrose, & Keshavan, 2011;Choi et al, 2008;Pentaraki et al, 2008), which may have limited sensitivity to capture subtle differences between FDRs and healthy controls (HCs).…”

Section: Introductionmentioning

confidence: 99%

“…Pattern classification studies have demonstrated that brain structural patterns help to identify individuals in at-risk mental states of psychosis and to predict disease transition (Fan, Shen, Gur, Gur, & Davatzikos, 2007;Koutsouleris et al, 2009;Pentaraki et al, 2008) at an individual level. Studies have demonstrated that large-scale FNs, in conjunction with pattern classification techniques, can distinguish SCZ patients from controls at the individual level (Fan et al, 2011;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

Jing

Ding

Lin

et al. 2019

Human Brain Mapping

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Schizophrenia (SCZ) patients and their unaffected first‐degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large‐scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs—the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus—were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave‐one‐out cross‐validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.

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Gender Differences in the Expression of Verbal Memory Deficits in Unaffected Parents of Patients With Schizophrenia

Cited by 2 publications

References 0 publications

“What” and “Where” in Auditory Sensory Processing: A High-Density Electrical Mapping Study of Distinct Neural Processes Underlying Sound Object Recognition and Sound Localization

“What” and “Where” in Auditory Sensory Processing: A High-Density Electrical Mapping Study of Distinct Neural Processes Underlying Sound Object Recognition and Sound Localization

Machine learning identifies unaffected first‐degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

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