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Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls - first steps in development of a biomarker.

Duffy FH, D'Angelo E, Rotenberg A, Gonzalez-Heydrich J - BMC Med (2015)

Bottom Line: DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7 %; CHR, 86.4 % correct).However, FMAER analysis showed no CON-CHR group differences.CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave, Boston, Massachusetts, 02115, USA. fhd@sover.net.

ABSTRACT

Background: Schizophrenia is a severe, disabling and prevalent mental disorder without cure and with a variable, incomplete pharmacotherapeutic response. Prior to onset in adolescence or young adulthood a prodromal period of abnormal symptoms lasting weeks to years has been identified and operationalized as clinically high risk (CHR) for schizophrenia. However, only a minority of subjects prospectively identified with CHR convert to schizophrenia, thereby limiting enthusiasm for early intervention(s). This study utilized objective resting electroencephalogram (EEG) quantification to determine whether CHR constitutes a cohesive entity and an evoked potential to assess CHR cortical auditory processing.

Methods: This study constitutes an EEG-based quantitative neurophysiological comparison between two unmedicated subject groups: 35 neurotypical controls (CON) and 22 CHR patients. After artifact management, principal component analysis (PCA) identified EEG spectral and spectral coherence factors described by associated loading patterns. Discriminant function analysis (DFA) determined factors' discrimination success between subjects in the CON and CHR groups. Loading patterns on DFA-selected factors described CHR-specific spectral and coherence differences when compared to controls. The frequency modulated auditory evoked response (FMAER) explored functional CON-CHR differences within the superior temporal gyri.

Results: Variable reduction by PCA identified 40 coherence-based factors explaining 77.8 % of the total variance and 40 spectral factors explaining 95.9 % of the variance. DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7 %; CHR, 86.4 % correct). The population distribution plotted along the canonical discriminant variable was clearly bimodal. Coherence factors delineated loading patterns of altered connectivity primarily involving the bilateral posterior temporal electrodes. However, FMAER analysis showed no CON-CHR group differences.

Conclusions: CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices. Symptoms of CHR may relate to altered connectivity with the posterior temporal regions but not to primary auditory processing abnormalities within these regions.

No MeSH data available.


Related in: MedlinePlus

Clinical high risk (CHR) and neurotypical control (CON) population distributions on the discriminant function analysis-derived canonical discriminant variable. Population distributions are shown for the CON (green) and CHR (red) groups. The X axis is the canonical discriminant, ranging from +4.5 to −3.5 units, which was created by the DFA process utilizing the eight factors described in Table 2, part (3). The Y axis represents subject number
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Fig4: Clinical high risk (CHR) and neurotypical control (CON) population distributions on the discriminant function analysis-derived canonical discriminant variable. Population distributions are shown for the CON (green) and CHR (red) groups. The X axis is the canonical discriminant, ranging from +4.5 to −3.5 units, which was created by the DFA process utilizing the eight factors described in Table 2, part (3). The Y axis represents subject number

Mentions: Two-group stepwise discriminant function analysis (BMDP 7 M) was instituted contrasting groups CON (n = 35) and CHR (n = 22), where coherence and spectral factors were allowed to enter (F to enter 4.0, to remove 3.996). Results are shown in Table 2. As evident, the F statistic approximation to Wilks’ Lambda was statistically significant (P ≤0.00001). Five coherence and three spectral factors were utilized (Table 3). The first three variables chosen and five of the eight chosen variables were coherence factors. Initial classification was 91.2 % correct overall (CON, 88.6 %; CHR, 95.5 % correct). The classification success upon completion of jackknifing was 86.0 % correct overall (CON, 85.7 %; CHR, 86.4 % correct). The groups where displayed as a histogram on the canonical discriminant variable (generated from the eight chosen factors) showing a mostly bimodal distribution (Fig. 4).Table 2


Neurophysiological differences between patients clinically at high risk for schizophrenia and neurotypical controls - first steps in development of a biomarker.

Duffy FH, D'Angelo E, Rotenberg A, Gonzalez-Heydrich J - BMC Med (2015)

Clinical high risk (CHR) and neurotypical control (CON) population distributions on the discriminant function analysis-derived canonical discriminant variable. Population distributions are shown for the CON (green) and CHR (red) groups. The X axis is the canonical discriminant, ranging from +4.5 to −3.5 units, which was created by the DFA process utilizing the eight factors described in Table 2, part (3). The Y axis represents subject number
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4630963&req=5

Fig4: Clinical high risk (CHR) and neurotypical control (CON) population distributions on the discriminant function analysis-derived canonical discriminant variable. Population distributions are shown for the CON (green) and CHR (red) groups. The X axis is the canonical discriminant, ranging from +4.5 to −3.5 units, which was created by the DFA process utilizing the eight factors described in Table 2, part (3). The Y axis represents subject number
Mentions: Two-group stepwise discriminant function analysis (BMDP 7 M) was instituted contrasting groups CON (n = 35) and CHR (n = 22), where coherence and spectral factors were allowed to enter (F to enter 4.0, to remove 3.996). Results are shown in Table 2. As evident, the F statistic approximation to Wilks’ Lambda was statistically significant (P ≤0.00001). Five coherence and three spectral factors were utilized (Table 3). The first three variables chosen and five of the eight chosen variables were coherence factors. Initial classification was 91.2 % correct overall (CON, 88.6 %; CHR, 95.5 % correct). The classification success upon completion of jackknifing was 86.0 % correct overall (CON, 85.7 %; CHR, 86.4 % correct). The groups where displayed as a histogram on the canonical discriminant variable (generated from the eight chosen factors) showing a mostly bimodal distribution (Fig. 4).Table 2

Bottom Line: DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7 %; CHR, 86.4 % correct).However, FMAER analysis showed no CON-CHR group differences.CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave, Boston, Massachusetts, 02115, USA. fhd@sover.net.

ABSTRACT

Background: Schizophrenia is a severe, disabling and prevalent mental disorder without cure and with a variable, incomplete pharmacotherapeutic response. Prior to onset in adolescence or young adulthood a prodromal period of abnormal symptoms lasting weeks to years has been identified and operationalized as clinically high risk (CHR) for schizophrenia. However, only a minority of subjects prospectively identified with CHR convert to schizophrenia, thereby limiting enthusiasm for early intervention(s). This study utilized objective resting electroencephalogram (EEG) quantification to determine whether CHR constitutes a cohesive entity and an evoked potential to assess CHR cortical auditory processing.

Methods: This study constitutes an EEG-based quantitative neurophysiological comparison between two unmedicated subject groups: 35 neurotypical controls (CON) and 22 CHR patients. After artifact management, principal component analysis (PCA) identified EEG spectral and spectral coherence factors described by associated loading patterns. Discriminant function analysis (DFA) determined factors' discrimination success between subjects in the CON and CHR groups. Loading patterns on DFA-selected factors described CHR-specific spectral and coherence differences when compared to controls. The frequency modulated auditory evoked response (FMAER) explored functional CON-CHR differences within the superior temporal gyri.

Results: Variable reduction by PCA identified 40 coherence-based factors explaining 77.8 % of the total variance and 40 spectral factors explaining 95.9 % of the variance. DFA demonstrated significant CON-CHR group difference (P <0.00001) and successful jackknifed subject classification (CON, 85.7 %; CHR, 86.4 % correct). The population distribution plotted along the canonical discriminant variable was clearly bimodal. Coherence factors delineated loading patterns of altered connectivity primarily involving the bilateral posterior temporal electrodes. However, FMAER analysis showed no CON-CHR group differences.

Conclusions: CHR subjects form a cohesive group, significantly separable from CON subjects by EEG-derived indices. Symptoms of CHR may relate to altered connectivity with the posterior temporal regions but not to primary auditory processing abnormalities within these regions.

No MeSH data available.


Related in: MedlinePlus