Abstract
Daily self-reported mood ratings from patients with bipolar disorder were analyzed
to see if the 60 days before an episode of hypomania or depression (pre-episode state)
could be distinguished from the 60 days before a month of euthymia (pre-remission
state), and if a pre-hypomanic state could be distinguished from a pre-depressed state.
Data were available from 98 outpatients with bipolar disorder, who returned about
one year of daily data, and received treatment as usual. The approximate entropy (ApEn),
mean mood and mood variability (standard deviation) were determined for 53 pre-hypomanic
states, 42 pre-depressive states, and 65 pre-remission states.There was greater serial
irregularity (ApEn) and greater variability in mood in the pre-episode than the pre-remission
state. There was greater serial irregularity (ApEn) but no difference in variability
in mood in the pre-hypomanic state when compared to the pre-depressed state. ApEn
can distinguish between the pre-episode, pre-remission, pre-hypomanic and pre-depressive
states. Using daily mood ratings, pre-episode changes were detected before the episode
onset. Further investigation to relate the pre-episode and pre-remission states to
other clinical and biological data is indicated.
References
1
Bauer M, Grof P, Gyulai L. et al .
Using technology to improve longitudinal studies: self-reporting with chronoRecord
in bipolar disorder.
Bipolar Disord.
2004;
6
67-74
2
Bauer M, Grof P, Rasgon N. et al .
Temporal relation between sleep and mood in patients with bipolar disorder.
Bipolar Disord.
2006;
8
160-167
3
Bauer M, Wilson T, Neuhaus K. et al .
Self-reporting software for bipolar disorder: validation of ChronoRecord by patients
with mania.
Psychiatry Res.
2008;
159
359-366
4
Bauer M, Glenn T, Grof P. et al .
Frequency of subsyndromal symptoms and employment status in patients with bipolar
disorder.
Soc Psychiatry Psychiatr Epidemiol.
2009;
44
515-522
5
Bauer M, Glenn T, Grof P. et al .
Subsyndromal mood symptoms: a useful concept for maintenance studies of bipolar disorder?.
Psychopathology.
2010;
43
1-7
6
Bhattacharya J.
Complexity analysis of spontaneous EEG.
Acta Neurobiol Exp (Wars).
2000;
60
495-501
7
Bhattacharya J.
Reduced degree of long-range phase synchrony in pathological human brain.
Acta Neurobiol Exp (Wars).
2001;
61
309-318
8
David AS.
Insight and psychosis.
Br J Psychiatry.
1990;
156
798-808
9
Denicoff KD, Smith-Jackson EE, Disney ER. et al .
Preliminary evidence of the reliability and validity of the prospective life-chart
methodology (LCM-p).
J Psychiatr Res.
1997;
31
593-603
10
Engle R.
Autoregressive conditional heteroskedasticity with estimates of the variance of United
Kingdom inflation.
Econometrica.
1982;
50
987-1007
11
Fischer JE, Bachmann LM, Jaeschke R.
A readers’ guide to the interpretation of diagnostic test properties: clinical example
of sepsis.
Intensive Care Med.
2003;
29
1043-1051
12
Glenn T, Whybrow PC, Rasgon N. et al .
Approximate entropy of self-reported mood prior to episodes.
Bipolar Disord.
2006;
8
424-429
13
Gottschalk A, Bauer MS, Whybrow PC.
Evidence of chaotic mood variation in bipolar disorder.
Arch Gen Psychiatry.
1995;
52
947-959
14
Jackson A, Cavanagh J, Scott J.
A systematic review of manic and depressive prodromes.
J Affect Disord.
2003;
74
209-217
15
Judd LL, Akiskal HS, Schettler PJ. et al .
The long-term natural history of the weekly symptomatic status of bipolar I disorder.
Arch Gen Psychiatry.
2002;
59
530-537
16
Mantere O, Suominen K, Valtonen HM. et al .
Only half of bipolar I and II patients report prodromal symptoms.
J Affect Disord.
2008;
111
366-371
17
O’Donnell BF, Hetrick WP, Vohs JL. et al .
Neural synchronization deficits to auditory stimulation in bipolar disorder.
Neuroreport.
2004;
15
1369-1372
18
Pincus SM.
Approximate entropy as a measure of system complexity.
Proc Natl Acad Sci USA.
1991;
88
2297-2301
19
Pincus SM, Gladstone IM, Ehrenkranz RA.
A regularity statistic for medical data analysis.
J Clin Monit.
1991;
7
335-345
20
Pincus SM, Viscarello RR.
Approximate entropy: a regularity measure for fetal heart rate analysis.
Obstet Gynecol.
1992;
79
249-255
21
Pincus SM, Gevers E, Robinson ICAF. et al .
Females secrete growth hormone with more process irregularity than males in both human
and rat.
Am J Physiol.
1996;
270
E107-E115
22
Pincus SM, Hartman ML, Roelfsema F. et al .
Hormone pulsatility discrimination via coarse and short time sampling.
Am J Physiol.
1999;
277
E948-E957
23
Pincus SM.
Irregularity and asynchrony in biologic network signals.
Methods Enzymol.
2000;
321
149-182
24
Tretter F, Gebicke-Haerter PJ.
Philosophy of neuroscience and options of systems science.
Pharmacopsychiatry.
2009;
42
(S 01)
S2-S10
25
Tschacher W, Scheier C, Hashimoto Y.
Dynamical analysis of schizophrenia courses.
Biol Psychiatry.
1997;
41
428-437
26
Vaillancourt DE, Newell KM.
Changing complexity in human behavior and physiology through aging and disease.
Neurobiol Aging.
2002;
23
1-11
27
Veldhuis JD, Keenan DM, Pincus SM.
Motivations and methods for analyzing pulsatile hormone secretion.
Endocr Rev.
2008;
29
823-864
28
Vikman S, Mäkikallio TH, Yli-Mäyry S. et al .
Altered complexity and correlation properties of R-R interval dynamics before the
spontaneous onset of paroxysmal atrial fibrillation.
Circulation.
1999;
100
2079-2084
29
Yen CF, Chen CS, Ko CH. et al .
Changes in insight among patients with bipolar I disorder: a 2-year prospective study.
Bipolar Disord.
2007;
9
238-242
30
Zweig MH, Campbell G.
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical
medicine.
Clinical Chemistry.
1993;
39
561-577
Correspondence
Prof. Dr. Dr. M. Bauer
Department of Psychiatry and
Psychotherapy
Universitätsklinikum Carl
Gustav Carus
Technische Universität Dresden
Fetscherstraße 74
01307 Dresden
Germany
Telefon: +49/351/45 80
Fax: +49/30/450 51 79 62
eMail: michael.bauer@uniklinikum-dresden.de