Medical Hypotheses MedrcolHypofheses(l991) 35, II-16 ‘in Longman Group UK Ltd 1991 -
Toward an Analysis of Conscious Activity: Evolution of Vigilance
1.
A. HIGASHI National
Institute
for Physiological
Sciences,
Okazaki
444,
Japan
Abstract - Previous articles (3-6) have presented two experimental methods - the Dynamic Behavioral Stage Analysis Method (DBSAM) and the Micro-sleep and Micro-wakefulness Processing Method (MMP) - as investigational approaches oriented in the direction of developing a theoretical concept. MMP employs a simplified method that uses duration of sleep and wakefulness (ti) as the parameter to alter the time units and reverse diagnose sleep and wakefulness as wakefulness and sleep, respectively. Results of vigilance (0) stalndard diagnosis and those of vigilance (2 n) standard diagnosis (evaluating apparent sleep and wakefulness based on vigilance (2 n)) werer compared using model data. This paper discusses the conceptual background necessary to understand the correlation between sleep, walkefulness, and consciousness, using model data. It also hypothesizes that conscious activity level, generated by sleep and wakefulness vectors, acts as an address specifier of inflormation that is input and output within the brain.
realistic enough to allow the experimental of conscious activity analysis.
Introduction Study of consciousness has been regarded as too difficult a phenomenon to be analyzed only in terms of traditional physiological methods, and reg,arded as the field of either philosophy or psychology. Animals used in experiments are unable to communicate with language, and they have very little ability to express themselves. One might, therefore, expect the development of a provisional, yet workable, theoretical concept that would make possible the experimental analysis of conscious activity when lower-order mammals are used in experiments for the study of higher order brain functions. Such a concept would have to be Date received 30 May 1990 Date accepted 15 August 1990
design
1. A new method using adjusted criteria: microsleep and micro-wakefulness processing (MMP) The duration of sleep and wakefulness in mice was diagnosed every second by DBSAM, in which entering sleep and awakening were included in the sleep stage category. Frequent repetitions of sleep and wakefulness are characteristic of small animals, particularly mice. Because slow and fast waves change frequently in the EEG of mice, diagnostic errors per unit time are greater than for encephalography in humans.
I1
12
MEDICAL
A method was devised in which very short periods of sleep or wakefulness lasting for less than ti seconds were ignored. Thus, rediagnosis at an arbitrarily altered level of diagnostic accuracy was achieved by establishing a diagnostic parameter as if the wakefulness (sleep) condition continued during sleep (wakefulness) when duration of condition did not last longer than ti s, (MSLP = tiS for wakefulness exchanged for sleep and MACT = ti s for sleep exchanged for wakefulness) (3, 5) (see Figs 1 and 2 in (3) and Fig. 1 in (5)). Figure 1 shows the application of micro-wakefulness (MACT) processing (A), and micro-sleep (MSLP) processing (B), where sleep (S) and wakefulness (W) stages in a mouse were diagnosed by DBSAM in the order S, W, S, W, S. MACT = ti (MSLP = ti) is defined as the artificial alteration of all wakefulness (sleep) stages of shorter than tis to sleep (wakefulness) stages. The S-W row on the top left side of Figure IA (B) was radiagnosed as the S-W (W-S) row on the bottom left side of Figure 1A (B). The right side of Figure 1A (B) shows in detail the duration and depth of
MICRO-SLREP
S
Re-
ui
sleep and wakefulness. The proportional relationship n = kti, where in the wakefulness stage k > 0 and in the sleep stage k < 0, was hypothesized for depth n and duration tl of sleep and wakefulness. In this case, rediagnosis when MACT = t2 (MSLP = t3) diagnoses changes in S and W at a new vigilance (0) standard (right ordinate), which is the left ordinate vigilance (0) standard raised by kt2 (lowered by kt,) to the vigilance (kt2) ((ktj)) level, as shown on the bottom right of Figure 1A (B). Rediagnosis does not diagnose the change of wakefulness duration t2 (sleep duration ti and tj) s into the sleep (wakefulness) stages shown by the dotted line(s) on the top right of Figure 1A (B). MACT and MSLP processing make it possible to modify or copy the way researchers interpret polygraphic tracings (frequency and amplitude) per unit time. It is useful for resolving disputes among researchers regarding the presence or absence of sleep-inducing effects of drugs if a relatively smaI1 ti vaIue is chosen. As the ti value increases, however, the conventional terms ‘sleep’ and ‘wakefulness’ become inappropriate. One of
AND MICRO-WAKEFLLNESSPROCESSING (MMP) Relation between sleep-wake depth and sleep-rake time /
pv\CTand NSLP parameters MACT=tr/ t4SLP.t. (tl < t,) /(tJ < t. < t,)
(4
HYPOTHESES
'
1’
I)
Vigilance O-shift (W; Wake/ s; Sleep)
~~,,..i\.,~*“,,,,,
t/IO-shin
diagnosla \ Clock time L
]( s
1st diagnosis t1
"
Clock time
L
(Apparent) sleep
I_t*~;
t3 KsLP=t, u-
Rediagnosis
w
’ s
l
n=kti
Fig. 1 Diagnosis using (B) involves diagnosis (t)), W (td) and S (ts) k < 0 is hypothesized
(W; k>O
/ S; k
Toward an Analysis of Conscious Activity: Evolution of Vigilance
1.
A. HIGASHI National
Institute
for Physiological
Sciences,
Okazaki
444,
Japan
Abstract - Previous articles (3-6) have presented two experimental methods - the Dynamic Behavioral Stage Analysis Method (DBSAM) and the Micro-sleep and Micro-wakefulness Processing Method (MMP) - as investigational approaches oriented in the direction of developing a theoretical concept. MMP employs a simplified method that uses duration of sleep and wakefulness (ti) as the parameter to alter the time units and reverse diagnose sleep and wakefulness as wakefulness and sleep, respectively. Results of vigilance (0) stalndard diagnosis and those of vigilance (2 n) standard diagnosis (evaluating apparent sleep and wakefulness based on vigilance (2 n)) werer compared using model data. This paper discusses the conceptual background necessary to understand the correlation between sleep, walkefulness, and consciousness, using model data. It also hypothesizes that conscious activity level, generated by sleep and wakefulness vectors, acts as an address specifier of inflormation that is input and output within the brain.
realistic enough to allow the experimental of conscious activity analysis.
Introduction Study of consciousness has been regarded as too difficult a phenomenon to be analyzed only in terms of traditional physiological methods, and reg,arded as the field of either philosophy or psychology. Animals used in experiments are unable to communicate with language, and they have very little ability to express themselves. One might, therefore, expect the development of a provisional, yet workable, theoretical concept that would make possible the experimental analysis of conscious activity when lower-order mammals are used in experiments for the study of higher order brain functions. Such a concept would have to be Date received 30 May 1990 Date accepted 15 August 1990
design
1. A new method using adjusted criteria: microsleep and micro-wakefulness processing (MMP) The duration of sleep and wakefulness in mice was diagnosed every second by DBSAM, in which entering sleep and awakening were included in the sleep stage category. Frequent repetitions of sleep and wakefulness are characteristic of small animals, particularly mice. Because slow and fast waves change frequently in the EEG of mice, diagnostic errors per unit time are greater than for encephalography in humans.
I1
12
MEDICAL
A method was devised in which very short periods of sleep or wakefulness lasting for less than ti seconds were ignored. Thus, rediagnosis at an arbitrarily altered level of diagnostic accuracy was achieved by establishing a diagnostic parameter as if the wakefulness (sleep) condition continued during sleep (wakefulness) when duration of condition did not last longer than ti s, (MSLP = tiS for wakefulness exchanged for sleep and MACT = ti s for sleep exchanged for wakefulness) (3, 5) (see Figs 1 and 2 in (3) and Fig. 1 in (5)). Figure 1 shows the application of micro-wakefulness (MACT) processing (A), and micro-sleep (MSLP) processing (B), where sleep (S) and wakefulness (W) stages in a mouse were diagnosed by DBSAM in the order S, W, S, W, S. MACT = ti (MSLP = ti) is defined as the artificial alteration of all wakefulness (sleep) stages of shorter than tis to sleep (wakefulness) stages. The S-W row on the top left side of Figure IA (B) was radiagnosed as the S-W (W-S) row on the bottom left side of Figure 1A (B). The right side of Figure 1A (B) shows in detail the duration and depth of
MICRO-SLREP
S
Re-
ui
sleep and wakefulness. The proportional relationship n = kti, where in the wakefulness stage k > 0 and in the sleep stage k < 0, was hypothesized for depth n and duration tl of sleep and wakefulness. In this case, rediagnosis when MACT = t2 (MSLP = t3) diagnoses changes in S and W at a new vigilance (0) standard (right ordinate), which is the left ordinate vigilance (0) standard raised by kt2 (lowered by kt,) to the vigilance (kt2) ((ktj)) level, as shown on the bottom right of Figure 1A (B). Rediagnosis does not diagnose the change of wakefulness duration t2 (sleep duration ti and tj) s into the sleep (wakefulness) stages shown by the dotted line(s) on the top right of Figure 1A (B). MACT and MSLP processing make it possible to modify or copy the way researchers interpret polygraphic tracings (frequency and amplitude) per unit time. It is useful for resolving disputes among researchers regarding the presence or absence of sleep-inducing effects of drugs if a relatively smaI1 ti vaIue is chosen. As the ti value increases, however, the conventional terms ‘sleep’ and ‘wakefulness’ become inappropriate. One of
AND MICRO-WAKEFLLNESSPROCESSING (MMP) Relation between sleep-wake depth and sleep-rake time /
pv\CTand NSLP parameters MACT=tr/ t4SLP.t. (tl < t,) /(tJ < t. < t,)
(4
HYPOTHESES
'
1’
I)
Vigilance O-shift (W; Wake/ s; Sleep)
~~,,..i\.,~*“,,,,,
t/IO-shin
diagnosla \ Clock time L
]( s
1st diagnosis t1
"
Clock time
L
(Apparent) sleep
I_t*~;
t3 KsLP=t, u-
Rediagnosis
w
’ s
l
n=kti
Fig. 1 Diagnosis using (B) involves diagnosis (t)), W (td) and S (ts) k < 0 is hypothesized
(W; k>O
/ S; k
