Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression.
Resting state functional connectivity subtypes predict discrete patterns of cognitive-affective functioning across levels of analysis among patients with treatment-resistant depression.
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DOI:
10.1016/j.brat.2021.103960
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发表时间:
2021-11
影响因子:
4.1
通讯作者:
Price RB
中科院分区:
文献类型:
--
作者:
Woody ML;Panny B;Degutis M;Griffo A;Price RB
Resting state functional connectivity (RSFC) in ventral affective (VAN), default mode (DMN) and cognitive control (CCN) networks may partially underlie heterogeneity in depression. The current study used data-driven parsing of RSFC to identify subgroups of patients with treatment-resistant depression (TRD; n=70) and determine if subgroups generalized to transdiagnostic measures of cognitive-affective functioning relevant to depression (indexed across self-report, behavioral, and molecular levels of analysis). RSFC paths within key networks were characterized using Subgroup-Group Iterative Multiple Model Estimation. Three connectivity-based subgroups emerged: Subgroup A, the largest subset and containing the fewest pathways; Subgroup B, containing unique bidirectional VAN/DMN negative feedback; and Subgroup C, containing the most pathways. Compared to other subgroups, subgroup B was characterized by lower self-reported positive affect and subgroup C by higher self-reported positive affect, greater variability in induced positive affect, worse response inhibition, and reduced striatal tissue iron concentration. RSFC-based categorization revealed three TRD subtypes associated with discrete aberrations in transdiagnostic cognitive-affective functioning that were largely unified across levels of analysis and were maintained after accounting for the variability captured by a disorder-specific measure of depressive symptoms. Findings advance understanding of transdiagnostic brain-behavior heterogeneity in TRD and may inform novel treatment targets for this population.
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DOI:
10.1176/appi.ajp.2015.14091200
发表时间:
2016-04-01
期刊:
The American journal of psychiatry
影响因子:
--
作者:
Clementz BA;Sweeney JA;Hamm JP;Ivleva EI;Ethridge LE;Pearlson GD;Keshavan MS;Tamminga CA
通讯作者:
Tamminga CA
影响因子:
82.9
作者:
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者:
Liston C
影响因子:
4.2
作者:
Erikson, KM;Jones, BC;Beard, JL
通讯作者:
Beard, JL
影响因子:
8.2
作者:
Akil H;Gordon J;Hen R;Javitch J;Mayberg H;McEwen B;Meaney MJ;Nestler EJ
通讯作者:
Nestler EJ
影响因子:
10.6
作者:
Hamilton JP;Farmer M;Fogelman P;Gotlib IH
通讯作者:
Gotlib IH