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         MindNet Journal - Vol. 1, No. 62a * [Part 1 of 4 parts]
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         V E R I C O M M / MindNet         "Quid veritas est?"
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    Editor: Mike Coyle

    Assistant Editor: Rick Lawler

    Research: Darrell Bross

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    MEMORY PROCESSES DESCRIBED AS BRAIN OSCILLATIONS IN THE EEG-ALPHA
    AND THETA BANDS

    By Wolfgang Klimesch

    Copyright 1995 Wolfgang Klimesch

    Reprinted by permission from:
    PSYCOLOQUY.95.6.06.memory-brain.1.klimesch.

    April 1995

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    Wolfgang Klimesch University of Salzburg Department of
    Physiological Psychology Institute of Psychology, Hellbrunnerstr.
    34 A-5020 Salzburg, AUSTRIA

    ABSTRACT: This target article tries to integrate results in
    memory research from diverse disciplines such as
    psychophysiology, cognitive psychology, anatomy and
    neurophysiology. The integrating link is seen in more recent
    anatomical findings that provide strong arguments for the
    assumption that oscillations provide the basic form of
    communication between cortical cell assemblies. The basic
    argument is that episodic memory processes, which are part of a
    complex working memory system, are reflected by oscillations in
    the theta band, whereas long-term memory processes are reflected
    by alpha oscillations. It is assumed that alpha and theta
    oscillations serve to encode, access, and retrieve cortical codes
    that are stored in the form of widely distributed but intensely
    interconnected cell assemblies.

    KEYWORDS: Alpha, EEG, Hippocampus, Memory, Oscillation, Thalamus,
    Theta.

    I. INTRODUCTION AND OVERVIEW

    1. In the following sections an attempt is made to link
    convergent knowledge about memory processes from four different
    fields: electrophysiology, cognitive psychology, functional
    anatomy, and neurophysiology. The motivation to follow up this
    interdisciplinary approach was spurred by the following two basic
    findings.

    2. The first finding is that the frequency of EEG-alpha
    oscillations is positively correlated with memory performance. In
    a series of four independent experiments, we (Klimesch, Schimke,
    Ladurner & Pfurtscheller, 1990; Klimesch, Schimke &
    Pfurtscheller, 1993; and Klimesch, Schimke, Doppelmayr, Ripper,
    Schwaiger & Pfurtscheller, 1994) were able to demonstrate that
    good memory performers have a significantly higher alpha
    frequency than subjects with bad memory performance. In
    concordance with these results, reaction time experiments have
    also shown that with increasing memory performance, retrieval
    time decreases (Klimesch, Schimke & Ladurner, 1988).

    3. The second finding stems from reaction time experiments with
    natural concepts. In a variety of different experiments
    (summarized in Klimesch, 1994), it was found that complex
    concepts can be processed much faster than less complex concepts
    and it could be demonstrated that this effect is not due to
    confounded variables such as typicality, word frequency or degree
    of connectivity. For cognitive psychology, this fact that more
    complex information can be processed faster than less complex
    information is a challenge, because well established theories
    (such as the well known ACT* theory from Anderson, 1983) and
    related experimental results show the opposite effect. In dealing
    with this challenge a new model, the connectivity model of
    semantic processing, was developed (Klimesch, 1987, 1994). The
    connectivity model focuses on an explicit description of
    representational assumptions for the encoding of long-term memory
    (LTM) codes and describes semantic search and retrieval processes
    in terms of a spreading activation process. It is important to
    emphasize that this positive speed effect is predicted and holds
    true for integrated (interconnected) semantic LTM codes but not
    for episodic short-term memory (STM) demands (Kroll & Klimesch,
    1992).

    4. Both findings point towards the importance of what will be
    termed the speed effect of memory performance: High memory
    performance and complex integrated knowledge speed up search and
    retrieval processes. With respect to the electrophysiological
    results, the interesting fact is that the connectivity model
    explains LTM performance in terms of an increase in the speed of
    the spreading activation process. It thus seems plausible to see
    a possible link with the finding that alpha frequency increases
    with increasing memory performance. Because there is evidence
    that EEG-alpha activity is related to thalamo-cortical
    oscillations (e.g., Steriade, Jones & Llinas, 1990) it is
    tempting to postulate a specific role of the thalamo-cortical
    network for memory processes.

    5. In trying to integrate these results within a
    psychophysiological perspective, we suggest the following
    preliminary hypothesis which rests on three assumptions: (1)
    Memory codes are stored in the form of interconnected but widely
    distributed networks (cell assemblies) in the cortex. (2) Memory
    codes are accessed and retrieved via "longitudinal" pathways
    linking deeper brain structures such as the thalamus with the
    cortex. And (3) alpha is one of the dominant rhythms reflecting
    the activity of some of these pathways. This hypothesis has one
    crucial implication and raises several questions. The most
    important questions are:

      - What is the neuro-anatomical and physiological basis for
    memory processes in general? (see section IV below).

      - Are different types of memory processes reflected by
    different types of oscillations? (see sections V and VI below).

    6. The crucial implication of the proposed hypothesis is that
    brain oscillations may be considered the basic phenomenon of
    cortical information processing. It should be noted that this
    implication is already inherent to the finding of a positive
    relationship between EEG-alpha frequency and memory performance.
    Because dominant brain oscillations can be recorded with the EEG,
    a summary of basic results is described in sections II.2 and II.3
    below. These sections will provide us with the necessary
    conceptual tools to describe memory processes in terms of
    oscillations.

    7. EEG findings that focus on the analysis of brain rhythms will
    be an important source of evidence for evaluating the proposed
    hypothesis. For reasons that are explained later in the text it
    will be assumed that EEG-alpha oscillations are closely related
    to semantic long-term memory (LTM) processes. Thus, when
    contrasting LTM with STM processes, the interesting question
    arises: Which frequency band in the EEG might be related to the
    encoding and retrieval of new information? It is well known that
    on the one hand, the hippocampal formation is of crucial
    importance for the encoding of new information and that on the
    other hand, the theta rhythm is the dominant oscillation of this
    brain region. It is, therefore, tempting to expect a specific
    relationship between the theta rhythm and the encoding and
    retrieval of new information.

    8. Besides some clinical findings (Arnolds, Lopes da Silva,
    Aitink, Kamp & Boeijinga, 1980) and recent results from our
    laboratory (Klimesch, Schimke & Schwaiger, 1994), there is a lack
    of clear-cut findings that point towards the proposed
    relationship between theta frequency and episodic STM demands in
    humans. This lack most likely is due to the fact that theta is
    not a dominant rhythm in the EEG of wake adults (see section V).
    Most of the results in the literature that deal with EEG and STM
    were reported in studies using event-related brain potentials
    (ERPs). The fact that there is no obvious link between ERP
    findings and brain oscillations brings us to the most speculative
    part of the manuscript. In a later section it is proposed that
    ERP-components that reflect certain STM demands may be due to
    phase locked theta activity (see section VI).

    II. BRAIN OSCILLATIONS: BASIC CONCEPTS AND HYPOTHESES

    9. The basic idea proposed in this and the following sections is
    that memory processes such as search, spreading activation, and
    retrieval can be described as processes that modulate the
    frequency of oscillatory neuronal discharge patterns. For sensory
    information processing it is well established that it is the
    modulation of the frequency of action potentials that encodes the
    information of a sensory input. This fact will be applied to
    cortical information processing. It will be assumed that the
    modulation of the frequency of brain oscillations is the basic
    mechanism of information transmission in the cortex.

    II.1 ARE OSCILLATIONS MANDATORY FOR CORTICAL INFORMATION
    PROCESSING?

    10. Braitenberg and his coworkers have shown that some of the
    conventional ideas about the anatomy of the cortex are wrong (see
    the comprehensive review in Braitenberg & Schuez, 1991). The
    question he and his group addresses refers to the issue of
    specificity or randomness of neuronal connections in the cortex.
    They demonstrate that the probability for an axonal synapse to
    have a particular neuron as postsynaptic partner is p = .001. The
    probability that more than one contact is made with a particular
    cell is extremely small (e.g., for three contacts the probability
    is p = .0000001). Given this enormous divergence in
    interconnectedness, one cell could never excite any other neuron.
    Due to the principle of temporal and spatial summation
    (summarized, e.g., in Koestner, 1985), a single cell will respond
    with an outgoing signal (an action potential) only if many
    convergent inputs are received at the same time (within a narrow
    time window). This means that converging neuronal signals must
    arrive synchronously in order to trigger an outgoing signal.

    11. The crucial question now is, which mechanisms operate to
    synchronize the neuronal input for each neuron? It is proposed
    that oscillations reflect this synchronizing mechanism. If
    signals come in synchronized bursts of action potentials, that
    is, in the form of oscillations, single neurons even in a
    distributed, randomly wired network will respond with an outgoing
    signal. Oscillations may be induced into a neuronal network by
    pacemaker cells and/or by endogenous membrane properties of
    individual cells (e.g., the reviews in Steriade, Jones & Llinas,
    1990; and Basar & Bullock, 1992). It is interesting to note that
    mathematical analyses indicate that, particularly in biological
    systems, oscillators tend to synchronize if their frequencies are
    not too different from each other (Strogatz & Stewart, 1993).

    II.2 THE FUNCTIONAL MEANING OF EEG SYNCHRONIZATION AND
    DESYNCHRONIZATION

    12. One of the best known results obtained with the EEG documents
    the functional importance of brain oscillations. Since the
    pioneering work of Berger in the late 1920s and early 1930s
    (e.g., Berger, 1929), it is known that the most dominant rhythm
    in the EEG, the alpha rhythm, can best be seen under conditions
    of relative mental inactivity, but it is blocked or
    desynchronized by attention and/or mental effort. The fact that a
    strong rhythm, such as the alpha rhythm, can be recorded from
    scalp electrodes means that millions of cortical neurons must
    oscillate synchronously with the same phase and within a
    comparatively narrow frequency band. Desynchronization, that is,
    the disappearance of the dominant alpha rhythm is functionally
    related to mental activity and means that different oscillators
    within the alpha band are no longer coupled. They oscillate with
    different phase lags and probably with different frequencies.
    This basic EEG-phenomenon of synchronization (during mental
    inactivity) and desynchronization (during mental activity)
    provides us with a preliminary but nonetheless important
    understanding of how information may be processed in the brain:
    The synchronization of very large populations of neurons
    oscillating with the same phase and frequency reflects a state in
    which no information is transmitted.

    II.3 TWO DIFFERENT TYPES OF SYNCHRONIZATION

    13. It is of crucial importance to emphasize that synchronization
    has two different meanings. Synchronization within the
    traditional context of EEG research reflects a state of mental
    inactivity. More recent research with microelectrodes implanted
    in the cortex, however, have shown that synchronous oscillatory
    discharge patterns in high frequency bands (such as the broad
    gamma band from 30 - 70 Hz) are related to rather localized
    cortical processes, reflecting cognitive activity such as visual
    encoding processes (e.g., Gray & Singer, 1987). Only at first
    glance do these two different meanings of neuronal
    synchronization seem to contradict each other. From the
    standpoint of EEG research, it  is a matter of resolution whether
    or not we may speak of synchronization or desynchronization.
    Desynchronization of the EEG is interpreted in terms of frequency
    and/or phase shifts of a large population of oscillators that
    become progressively uncoupled. Thus, recorded from EEG
    macroelectrodes, neuronal activity appears desynchronized.
    Nonetheless, within small cortical areas, neuronal activity may
    still exhibit a synchronous discharge pattern. To avoid
    confusion, we will call the synchronous activity of large
    cortical areas reflecting mental inactivity type 1
    synchronization. With type 2 synchronization we denote the
    synchronous oscillatory discharge pattern of selected and
    comparatively small cortical areas.

    14. In summarizing these three terms, we have to keep in mind
    that on the microscale, a frequency change (possibly over a broad
    frequency range) of a comparatively small group of neurons (or
    cell assembly) occurring synchronously within the different
    neurons of this group (cell assembly) reflects the processing of
    information (see also section III.2). Because information
    processing generally is considered a distributed process, a great
    number of different, distributed cell assemblies will show type 2
    synchronization in response to cognitive demands. On the
    macroscale, however, the behavior of many different cell
    assemblies responding with type 2 synchronization will show up as
    desynchronization in the EEG. The main reason for this is that
    each cell assembly will respond with its own frequency and this
    synchronization may not be coupled between cell assemblies.
    Because the synchronous discharge of small cell assemblies is a
    very weak signal for the EEG, type 2 synchronization can be
    detected primarily by microelectrodes but is difficult to detect
    with EEG macroelectrodes. Thus, if many different cell assemblies
    show uncoupled type 2 synchronization, the EEG will be
    de-synchronized. In contrast, type 1 synchronization is a very
    strong signal for the EEG, showing the synchronous, phase coupled
    oscillatory discharge pattern within a narrow frequency band of
    very large cell populations.

    15. EEG-frequencies are conventionally subdivided in frequency
    bands such as the theta (4 - 8 Hz), alpha (8 - 13 Hz), beta (14
    to about 30 Hz) and gamma bands (30 - 70 Hz). It is important to
    note that the traditional terms of EEG (type 1) synchronization
    and desynchronization apply for the alpha and beta bands only.
    The gamma band seems to synchronize in response to cognitive
    demands (Pfurtscheller, Flotzinger & Neuper, 1994) and seems to
    reflect real type 2 synchronization in the EEG (see Pulvermueller
    et al., 1994)., The theta band clearly synchronizes in response
    to cognitive demands.

    16. This basic behavior of the EEG generally is very similar for
    animals and humans (see e.g., the review in Lopes da Silva, 1992)
    with the exception that the frequency range of the theta rhythm
    is much wider in animals (lower mammals). Thus, to avoid
    confusions with the human EEG, the term rhythmic slow activity
    (RSA) was introduced to denote synchronized theta activity (see,
    e.g., Vanderwolf & Robinson, 1981) whereas the term irregular
    slow activity (ISA) is used to denote desynchronized theta
    activity. In contrast to ISA, RSA reflects a state of mental
    activity in animals. In humans too, theta synchronization is
    related to mental activity and to the encoding of episodic
    information in particular (see Klimesch, Schimke & Schwaiger,
    1994; and Arnolds et al., 1980).

    17. In the human EEG of wake adults, theta is a weak rhythm that
    most likely is induced into the cortex via a small but
    distributed set of longitudinal hippocampo-cortical pathways (see
    sections III, V and the review in Lopes da Silva, 1992). In wake
    adults, theta synchronization seems to have the meaning of
    coupled type 2 synchronization. This sort of synchronization is
    explained in terms of a small subset of hippocampo-cortical
    feedback loops responding to an appropriate event or signal with
    synchronized phase locked theta activity. As a result, selected
    and distributed cortical cell assemblies will start to respond
    with synchronous theta activity. According to this
    interpretation, theta desynchronization (ISA in animals) simply
    is the lack of type 2 synchronization (RSA in animals). It is
    well known, however, that during certain sleep stages, theta
    becomes a dominant rhythm in the EEG, reflecting type 1
    synchronization. Note that this is not in contradiction to the
    proposed interpretation: Type 2 theta synchronization reflects
    the processing of information whereas type 1 theta
    synchronization reflects the lack of information processing or a
    state of "functional inhibition".

    18. A good example to document the meaningfulness of type 1
    synchronization for memory processes is the general issue of
    inhibition. Any memory theory trying to explain search processes
    is confronted with the fundamental problem of how it can be
    explained that spreading activation is confined to the relevant
    parts of the (cortical) memory network (Klimesch, 1994). The most
    obvious way of handling this problem is to assume strong
    inhibitory processes that allow a search process to spread only
    within certain regions of the network. According to Braitenberg &
    Schuez (1991), however, the assumption of powerful inhibitory
    processes is not plausible, given the fact that about 85% of all
    cortical neurons are pyramidal cells with excitatory synapses.
    Inhibitory synapses are comparatively rare (comprising only 11 to
    15% of all cortical synapses) and reside on stellate cells that
    primarily make local connections. Thus, in the cortical network,
    inhibitory processes are more likely operating locally and
    probably do not have far reaching effects. Synchronous (type 1)
    oscillations within a narrow frequency band, induced in large
    areas or even in the entire cortex (e.g., in sleep), may have
    powerful inhibitory effects. When synchronous (type 1)
    oscillations within a narrow frequency band are selectively
    induced in certain cortical areas from a part in the brain that
    operates as some kind of "monitoring unit" or "control unit", the
    basic theoretical framework for explaining inhibitory processes
    by type 1 synchronization is at hand. According to this idea,
    type 1 synchronization could act to block a search process
    (reflected by type 2 synchronization) from entering irrelevant
    parts of the network.

    [Continued to part 2]
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