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Characterizing cognitive heterogeneity on the schizophrenia–bipolar disorder spectrum

Published online by Cambridge University Press:  28 February 2017

T. E. Van Rheenen ,
K. E. Lewandowski ,
E. J. Tan ,
L. H. Ospina ,
D. Ongur ,
E. Neill ,
C. Gurvich ,
C. Pantelis ,
A. K. Malhotra  and
S. L. Rossell
...Show all authors
T. E. Van Rheenen*
Affiliation:
Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton, VIC, Australia Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
K. E. Lewandowski
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
E. J. Tan
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
L. H. Ospina
Affiliation:
Icahn School of Medicine, Mount Sinai, NY, USA
D. Ongur
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
E. Neill
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Department of Psychiatry, St Vincent's Hospital, Melbourne, VIC, Australia
C. Gurvich
Affiliation:
Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
C. Pantelis
Affiliation:
Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton, VIC, Australia Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia Centre for Neural Engineering (CfNE), Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
A. K. Malhotra
Affiliation:
Hofstra Northwell School of Medicine, Hempstead, NY, USA
S. L. Rossell
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia Department of Psychiatry, St Vincent's Hospital, Melbourne, VIC, Australia
K. E. Burdick
Affiliation:
Icahn School of Medicine, Mount Sinai, NY, USA James J Peters VA Hospital, NY, USA
*
*Address for correspondence: T. Van Rheenen, Ph.D., Melbourne Neuropsychiatry Centre, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton, VIC 3053, Australia. (Email: [email protected])
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Abstract

Background

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored.

Method

Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).

Results

Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently.

Conclusions

Quantitative SZ–BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Keywords

Clusteringcognitionheterogeneityneuropsychologypsychosis spectrum
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 10 , July 2017 , pp. 1848 - 1864
Copyright
Copyright © Cambridge University Press 2017 

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References

Altman, EG, Hedeker, DR, Janicak, PG, Peterson, JL, Davis, JM (1994). The Clinician-Administered Rating Scale for Mania (CARS-M): development, reliability, and validity. Biological Psychiatry 36, 124–134.CrossRefGoogle ScholarPubMed
Barnett, J, Salmond, C, Jones, P, Sahakian, B (2006). Cognitive reserve in neuropsychiatry. Psychological Medicine 36, 1053–1064.CrossRefGoogle ScholarPubMed
Blair, JR, Spreen, O (1989). Predicting premorbid IQ: a revision of the National Adult Reading Test. Clinical Neuropsychologist 3, 129–136.CrossRefGoogle Scholar
Bora, E, Hıdıroğlu, C, Özerdem, A, Kaçar, ÖF, Sarısoy, G, Civil Arslan, F, Aydemir, Ö, Cubukcuoglu Tas, Z, Vahip, S, Atalay, A, Atasoy, N, Ateşci, F, Tümkaya, S (2016). Executive dysfunction and cognitive subgroups in a large sample of euthymic patients with bipolar disorder. European Neuropsychopharmacology 26, 1338–1347.CrossRefGoogle Scholar
Bora, E, Pantelis, C (2015). Meta-analysis of cognitive impairment in first-episode bipolar disorder: comparison with first-episode schizophrenia and healthy controls. Schizophrenia Bulletin 41, 1095–1104.CrossRefGoogle ScholarPubMed
Bora, E, Yucel, M, Pantelis, C (2009 a). Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. Journal of Affective Disorders 113, 1–20.CrossRefGoogle ScholarPubMed
Bora, E, Yucel, M, Pantelis, C (2009 b). Cognitive functioning in schizophrenia, schizoaffective disorder and affective psychoses: meta-analytic study. British Journal of Psychiatry 195, 475–482.CrossRefGoogle ScholarPubMed
Bora, E, Yücel, M, Pantelis, C (2010). Neurocognitive markers of psychosis in bipolar disorder: a meta-analytic study. Journal of Affective Disorders 127, 1–9.CrossRefGoogle ScholarPubMed
Burdick, KE, Goldberg, TE, Cornblatt, BA, Keefe, RS, Gopin, CB, Derosse, P, Braga, RJ, Malhotra, AK (2011). The MATRICS Consensus Cognitive Battery in patients with bipolar I disorder. Neuropsychopharmacology 36, 1587–1592.CrossRefGoogle ScholarPubMed
Burdick, KE, Russo, M, Frangou, S, Mahon, K, Braga, RJ, Shanahan, M, Malhotra, AK (2014). Empirical evidence for discrete neurocognitive subgroups in bipolar disorder: clinical implications. Psychological Medicine 44, 3083–3096.CrossRefGoogle ScholarPubMed
Clementz, BA, Sweeney, JA, Hamm, JP, Ivleva, EI, Ethridge, LE, Pearlson, GD, Keshavan, MS, Tamminga, CA (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. American Journal of Psychiatry 173, 373–384.CrossRefGoogle ScholarPubMed
Craddock, N, O'Donovan, MC, Owen, MJ (2006). Genes for schizophrenia and bipolar disorder? Implications for psychiatric nosology. Schizophrenia Bulletin 32, 9–16.CrossRefGoogle ScholarPubMed
Douglas, KM, Van Rheenen, TE (2016). Current treatment options for cognitive impairment in bipolar disorder: a review. Current Treatment Options in Psychiatry 3, 330–355.CrossRefGoogle Scholar
First, MB, Spitzer, RL, Gibbon, M, Williams, JB (1996). Structured Clinical Interview for DSM-IV Axis I Disorders Clinician Version (SCID-CV). American Psychiatric Press: Washington, DC.Google Scholar
Gale, CR, Deary, IJ, Boyle, SH, Barefoot, J, Mortensen, LH, Batty, G (2008). Cognitive ability in early adulthood and risk of 5 specific psychiatric disorders in middle age: the Vietnam Experience Study. Archives of General Psychiatry 65, 1410–1418.CrossRefGoogle ScholarPubMed
Garrett, DD, Kovacevic, N, McIntosh, AR, Grady, CL (2011). The importance of being variable. Journal of Neuroscience 31, 4496–4503.CrossRefGoogle ScholarPubMed
Green, M, Cairns, M, Wu, J, Dragovic, M, Jablensky, A, Tooney, P, Scott, R, Carr, V (2013). Genome-wide supported variant MIR137 and severe negative symptoms predict membership of an impaired cognitive subtype of schizophrenia. Molecular Psychiatry 18, 774–780.CrossRefGoogle ScholarPubMed
Hamilton, M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Harvey, PD, Aslan, M, Du, M, Zhao, H, Siever, LJ, Pulver, A, Gaziano, JM, Concato, J (2016). Factor structure of cognition and functional capacity in two studies of schizophrenia and bipolar disorder: implications for genomic studies. Neuropsychology 30, 28–39.CrossRefGoogle ScholarPubMed
Harvey, PD, Siever, LJ, Huang, GD, Muralidhar, S, Zhao, H, Miller, P, Aslan, M, Mane, S, McNamara, M, Gleason, T, Brophy, M, Przygodszki, R, O'Leary, TJ, Gaziano, M, Concato, J (2014). The genetics of functional disability in schizophrenia and bipolar illness: methods and initial results for VA cooperative study #572. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 165B, 381–389.CrossRefGoogle Scholar
Hatzimanolis, A, Bhatnagar, P, Moes, A, Wang, R, Roussos, P, Bitsios, P, Stefanis, CN, Pulver, AE, Arking, DE, Smyrnis, N, Stefanis, NC, Avramopoulos, D (2015). Common genetic variation and schizophrenia polygenic risk influence neurocognitive performance in young adulthood. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 168B, 392–401.CrossRefGoogle ScholarPubMed
Hill, SK, Reilly, JL, Keefe, RSE, Gold, JM, Bishop, JR, Gershon, ES, Tamminga, CA, Pearlson, GD, Keshavan, MS, Sweeney, JA (2013). Neuropsychological impairments in schizophrenia and psychotic bipolar disorder: findings from the Bipolar–Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. American Journal of Psychiatry 170, 1275–1284.CrossRefGoogle ScholarPubMed
Hill, WD, Davies, G; CHARGE Cognitive Working Group, Liewald, DC, McIntosh, AM, Deary, IJ (2016). Age-dependent pleiotropy between general cognitive function and major psychiatric disorders. Biological Psychiatry 80, 266–273.CrossRefGoogle ScholarPubMed
Holdnack, JA (2001). Wechsler Test of Adult Reading. The Psychological Corporation: San Antonio, TX.Google Scholar
International Schizophrenia Consortium, Purcell, SM, Wray, NR, Stone, JL, Visscher, PM, O'Donovan, MC, Sullivan, PF, Sklar, P (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752.Google ScholarPubMed
Jastak, S, Wilkinson, GS (1984). The Wide Range Achievement Test – Revised Administration Manual. Jastak Associates: Wilmington, DE.Google Scholar
Jensen, JH, Knorr, U, Vinberg, M, Kessing, LV, Miskowiak, KW (2016). Discrete neurocognitive subgroups in fully or partially remitted bipolar disorder: associations with functional abilities. Journal of Affective Disorders 205, 378–386.CrossRefGoogle ScholarPubMed
Kälin, AM, Pflüger, M, Gietl, AF, Riese, F, Jäncke, L, Nitsch, RM, Hock, C (2014). Intraindividual variability across cognitive tasks as a potential marker for prodromal Alzheimer's disease. Frontiers in Aging Neuroscience 6, 147.CrossRefGoogle ScholarPubMed
Kay, SR, Flszbein, A, Opfer, LA (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13, 261–276.CrossRefGoogle ScholarPubMed
Kern, RS, Nuechterlein, KH, Green, MF, Baade, LE, Fenton, WS, Gold, JG, Keefe, RSE, Mesholam-Gately, R, Mintz, J, Seidman, LJ, Stover, E, Marder, SR (2008). The MATRICS Consensus Cognitive Battery, part 2: co-norming and standardization. American Journal of Psychiatry 165, 214–220.CrossRefGoogle ScholarPubMed
Lacritz, LH, Cullum, CM (1998). The Hopkins Verbal Learning Test and CVLT: a preliminary comparison. Archives of Clinical Neuropsychology 13, 623–628.Google ScholarPubMed
Lacritz, LH, Cullum, CM, Weiner, MF, Rosenberg, RN (2001). Comparison of the Hopkins Verbal Learning Test-revised to the California Verbal Learning Test in Alzheimer's disease. Applied Neuropsychology 8, 180–184.CrossRefGoogle Scholar
Lencz, T, Knowles, E, Davies, G, Guha, S, Liewald, DC, Starr, JM, Djurovic, S, Melle, I, Sundet, K, Christoforou, A, Reinvang, I, Mukherjee, S, DeRosse, P, Lundervold, A, Steen, VM, John, M, Espeseth, T, Räikkönen, K, Widen, E, Palotie, A, Eriksson, JG, Giegling, I, Konte, B, Ikeda, M, Roussos, P, Giakoumaki, S, Burdick, KE, Payton, A, Ollier, W, Horan, M, Donohoe, G, Morris, D, Corvin, A, Gill, M, Pendleton, N, Iwata, N, Darvasi, A, Bitsios, P, Rujescu, D, Lahti, J, Hellard, SL, Keller, MC, Andreassen, OA, Deary, IJ, Glahn, DC, Malhotra, AK (2014). Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT). Molecular Psychiatry 19, 168–174.CrossRefGoogle Scholar
Leucht, S, Kane, JM, Etschel, E, Kissling, W, Hamann, J, Engel, RR (2006). Linking the PANSS, BPRS, and CGI: clinical implications. Neuropsychopharmacology 31, 2318–2325.CrossRefGoogle ScholarPubMed
Leucht, S, Rothe, P, Davis, J, Engel, R (2013). Equipercentile linking of the BPRS and the PANSS. European Neuropsychopharmacology 23, 956–959.CrossRefGoogle ScholarPubMed
Lewandowski, KE, Sperry, SH, Cohen, BM, Öngür, D (2014). Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis. Psychological Medicine 44, 3239–3248.CrossRefGoogle ScholarPubMed
MacDonald, SWS, Li, S-C, Bäckman, L (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging 24, 792–808.CrossRefGoogle ScholarPubMed
MacDonald, SWS, Nyberg, L, Bäckman, L (2006). Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences 29, 474–480.CrossRefGoogle ScholarPubMed
Montgomery, SA, Åsberg, M (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry 134, 382–389.CrossRefGoogle ScholarPubMed
Morgan, EE, Woods, SP, Delano-Wood, L, Bondi, MW, Grant, I; HIV Neurobehavioral Research Program (HNRP) Group (2011). Intraindividual variability in HIV infection: evidence for greater neurocognitive dispersion in older HIV seropositive adults. Neuropsychology 25, 645–654.CrossRefGoogle ScholarPubMed
Overall, JE, Gorham, DR (1962). The Brief Psychiatric Rating Scale. Psychological Reports 10, 799–812.CrossRefGoogle Scholar
Pearlson, GD, Clementz, BA, Sweeney, JA, Keshavan, MS, Tamminga, CA (2016). Does biology transcend the symptom-based boundaries of psychosis? Psychiatric Clinics of North America 39, 165–174.CrossRefGoogle ScholarPubMed
Reser, MP, Allott, KA, Killackey, E, Farhall, J, Cotton, SM (2015). Exploring cognitive heterogeneity in first-episode psychosis: what cluster analysis can reveal. Psychiatry Research 229, 819–827.CrossRefGoogle ScholarPubMed
Rossell, SL, Van Rheenen, TE (2013). Theory of mind performance using a story comprehension task in bipolar mania compared to schizophrenia and healthy controls. Cognitive Neuropsychiatry 18, 409–421.CrossRefGoogle Scholar
Ruocco, AC, Reilly, JL, Rubin, LH, Daros, AR, Gershon, ES, Tamminga, CA, Pearlson, GD, Hill, SK, Keshavan, MS, Gur, RC, Sweeney, JA (2014). Emotion recognition deficits in schizophrenia-spectrum disorders and psychotic bipolar disorder: findings from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study. Schizophrenia Research 158, 105–112.CrossRefGoogle ScholarPubMed
Schretlen, DJ, Peña, J, Aretouli, E, Orue, I, Cascella, NG, Pearlson, GD, Ojeda, N (2013). Confirmatory factor analysis reveals a latent cognitive structure common to bipolar disorder, schizophrenia, and normal controls. Bipolar Disorders 15, 422–433.CrossRefGoogle ScholarPubMed
Sheehan, DV, Lecrubier, Y, Harnett-Sheehan, K, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R, Dunbar, GC (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59 (Suppl. 20), 22–33.Google ScholarPubMed
Solé, B, Jiménez, E, Torrent, C, Mar Bonnin, C, Torres, I, Reinares, M, Priego, Á, Salamero, M, Colom, F, Varo, C, Vieta, E, Martínez-Arán, A (2016). Cognitive variability in bipolar II disorder: who is cognitively impaired and who is preserved. Bipolar Disorders 18, 288–299.CrossRefGoogle ScholarPubMed
Sperry, SH, O'Connor, LK, Öngür, D, Cohen, BM, Keshavan, MS, Lewandowski, KE (2015). Measuring cognition in bipolar disorder with psychosis using the MATRICS Consensus Cognitive Battery. Journal of the International Neuropsychological Society 21, 468–472.CrossRefGoogle ScholarPubMed
Stern, Y (2009). Cognitive reserve. Neuropsychologia 47, 2015–2028.CrossRefGoogle ScholarPubMed
Tamminga, CA, Ivleva, EI, Keshavan, MS, Pearlson, GD, Clementz, BA, Witte, B, Morris, DW, Bishop, J, Thaker, GK, Sweeney, JA (2013). Clinical phenotypes of psychosis in the Bipolar–Schizophrenia Network on Intermediate Phenotypes (B-SNIP). American Journal of Psychiatry 170, 1263–1274.CrossRefGoogle ScholarPubMed
Tamminga, CA, Pearlson, G, Keshavan, M, Sweeney, J, Clementz, B, Thaker, G (2014). Bipolar And Schizophrenia Network For Intermediate Phenotypes: outcomes across the psychosis continuum. Schizophrenia Bulletin 40, S131–S137.CrossRefGoogle Scholar
Tan, EJ, Rossell, SL (2014). Building a neurocognitive profile of thought disorder in schizophrenia using a standardized test battery. Schizophrenia Research 152, 242–245.CrossRefGoogle ScholarPubMed
Van Rheenen, TE, Bryce, S, Tan, EJ, Neill, E, Gurvich, C, Louise, S, Rossell, SL (2016). Does cognitive performance map to categorical diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder? A discriminant functions analysis. Journal of Affective Disorders 192, 109–115.CrossRefGoogle ScholarPubMed
Van Rheenen, TE, Rossell, SL (2014). An empirical evaluation of the MATRICS Consensus Cognitive Battery in bipolar disorder. Bipolar Disorders 16, 318–325.CrossRefGoogle ScholarPubMed
Weickert, TW, Goldberg, TE, Gold, JM, Bigelow, LB, Egan, MF, Weinberger, DR (2000). Cognitive impairments in patients with schizophrenia displaying preserved and compromised intellect. Archives of General Psychiatry 57, 907–913.CrossRefGoogle ScholarPubMed
Wells, R, Swaminathan, V, Sundram, S, Weinberg, D, Bruggemann, J, Jacomb, I, Cropley, V, Lenroot, R, Pereira, AM, Zalesky, A, Bousman, C, Pantelis, C, Weickert, CS, Weickert, TW (2015). The impact of premorbid and current intellect in schizophrenia: cognitive, symptom, and functional outcomes. NPJ Schizophrenia 1, 15043.CrossRefGoogle ScholarPubMed
Young, R, Biggs, J, Ziegler, V, Meyer, D (1978). A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429–435.CrossRefGoogle ScholarPubMed
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