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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5553_Библиотеки_им_академика_М_И_Перельмана
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neurophysiological effects of that impair patients’ ability to
perform attention-demanding cognitive tasks.
In the next stage, Cook et al. collected each participant’s
neuroimaging (f and ) and supplementary non-imaging data.
Such multimodal data acquisition is a standard approach in f
studies because non-imaging data are subsequently incorporated into
neuroimaging analyses (Muhr 2022). Yet, the comprehensiveness
with which Cook et al. implemented this approach deserves attention.
What stands out is the level of detail with which they quantified the
participants’ exercise-induced changes in symptoms while
consistently relating these changes to patients’ subjective reports of
. On day one of testing, besides acquiring clinical data, Cook et
al. (2017, 89) characterized the patients’ subjective experience
of heterogeneous symptoms (including muscle pain, unrefreshing
sleep, fatigue, and memory problems) at baseline using three
complementary self-report questionnaires. Directly before and 24
hours after the exercise, participants were administered a visual self-
report scale that measured the subjective intensity of ten major
symptoms derived from the Carruthers et al. (2003) criteria.
Furthermore, while the participants performed the exercise, the
researchers collected their cardiorespiratory (oxygen consumption
and heart rate), perceptual (subjective rating of perceived exertion
and leg muscle pain), and lactate data.
The neuroimaging data were collected on two separate occasions.
Each participant’s pre- and post-exercise neuroimaging procedures
started with the generation of high-resolution anatomical scans,
followed by a 25-minute acquisition of f data during the
performance of the task sequence (Cook et al. 2017, 89). Over 25
minutes, an f volume that consisted of 40 sagittal slices was
collected every two seconds, resulting in a voxel resolution of 3.75 ×
3.75 × 5 millimeters. Simultaneously, the participants’ task
performance was monitored for correct, incorrect, and omitted
responses. During breaks within the tasks, which were divided into
blocks, participants were repeatedly asked to rate their perceived
levels of mental fatigue. It is apparent that this comprehensive
multimodal data collection aimed to integrate various physiological
(i.e., cardiopulmonary and lactate data), neurophysiological (i.e.,
brain activity), and behavioral measurements (i.e., cognitive
performance) with the patients’ self-reported subjective experiences
of the exercise-induced changes in their physical and mental states.
Importantly, this integration was pivotal to the researchers’ efforts to
articulate the neurophysiological basis of patients’ subjective
experience of .
The fourth stage of articulation involved a long chain of interlinked
and increasingly complex statistical data analyses. First, the pre- and
post-exercise non-imaging data were analyzed using descriptive
statistics and then compared between the patient and control groups.
These analyses revealed that, despite reporting greater exertion,
patients cycled at significantly lower speeds than controls and, unlike
healthy controls, experienced a pronounced exacerbation of all
symptoms 24 hours after the exercise (Cook et al. 2017, 90– 91).
Another relevant finding was that the cognitive performance during
scanning improved in control subjects pre- to post-exercise, whereas
it significantly decreased in patients. In other words, while controls
learned to better perform the through practice, patients did
not.
However, the most insightful results were obtained by analyzing the
f data. After applying standard preprocessing steps—including
motion correction and normalization—that are required to make the
individual subjects’ f data mutually comparable (Muhr 2022), Cook
et al. first performed whole-brain single-subject analyses for each
participant. The resulting single-subject maps served as input for
subsequent group-level analyses. In group-level analyses, individual
subjects’ idiosyncratic task-induced neural responses are treated as
noise and eliminated, while amplifying the neural responses shared
across the subjects within the group (Muhr 2022, 358). Such
statistical aggregation of group effects is meant to identify the
disease-specific neural mechanisms common to all patients. Here, the
group-level analyses were limited to the predefined regions of interest
() that, based on their previous f studies of , Cook et al.
(2017, 90) hypothesized would be differentially activated in patients
and control subjects. They chose the specific that, according to
broader neuroimaging research, partake in motor activity, attention,
working memory, and executive functions. Notably, this choice of
was aligned with the cognitive functions that the experimental
tasks, including the , were designed to isolate.
Using the predefined , Cook et al. performed numerous
group-level analyses. For each of the three experimental tasks,
FIG . 4
they generated separate f maps that showed the distribution of
task-induced statistically relevant brain activations—first at baseline,
then post-exercise. They further computed f maps for within-
group comparisons of the changes in task-induced brain responses
from pre- to post-exercise for each group and each task separately.
Next, they produced f maps for between-group comparisons of
the changes in task-induced brain responses from pre- to post-
exercise for each task separately. Finally, they performed post-hoc
analyses to explore “the relationships between metabolic and
behavioral data during exercise and brain responses to the
post-exercise” (Cook et al. 2017, 93).
FIG. 4 Chain of operations underpinning the articulation of neural underpinnings
of in Cook et al. (2017). Courtesy of Paula Muhr
In short, to articulate the neural effects of from their f
data, Cook et al. deployed a chain of stratified neuroimaging
analyses involving multiple targeted multilevel comparisons within and
between groups and pre- versus post-exercise for their three
experimental tasks. Each group-level analysis resulted in a separate
f map that provided only a partial empirical perspective on the
neural effects of . Therefore, to interpret their empirical findings,
Cook et al. had to compare the different f maps to one another
and thus synthesize their partial, yet complementary, perspectives.
The study’s neuroimaging findings are too complex to examine in full
detail. Overall, the f maps jointly indicated that the differences in
task-induced brain responses between patients and healthy controls,
and the changes in task-induced brain responses from pre- to post-
exercise in both groups, increased with the task difficulty, becoming
most pronounced for the cognitively fatiguing . The between-
group maps for the were taken to show that, relative to
healthy controls, patients exhibited significantly heightened brain
responses post-exercise in the brain regions “critical for efficient
cognitive processing involving processes associated with attention,
error detection, and cognitive control” (Cook et al. 2017, 96).
Additionally, the within-group maps revealed that, after the exercise,
healthy controls had decreased neural activity and greater
performance accuracy on the , whereas patients showed the
opposite development—their cognitive performance declined while
their neural activity intensified. Most importantly, the brain maps
suggested that the patients’ self-reported post-exercise fatigue
exacerbation “was significantly and positively related to brain
responses in the right inferior parietal and superior temporal cortices”
(Cook et al. 2017, 93), thus linking patients’ subjective experience of
to specific neurophysiological changes.
In sum, the multiple complementary f maps that Cook et al.
generated through a chain of interrelated operations provided
preliminary, albeit tentative, empirical insights into possible neural
underpinnings of , implicating multiple brain regions. These
images also offered cumulative visual support for the physiological
reality of patients’ subjectively experienced exertion-induced
worsening of symptoms. As demonstrated in the preceding
analysis, the ability of f maps to fulfill such epistemic functions
hinged on the interlinked operations that went into their production,
allowing the articulation of the ’s neural effects in these images.
Thus, far from providing direct access to ’s neural effects, the
construction of the highly mediated referentiality of these operative
images required extensive work and many decisions by researchers,
which had to be justified in relation to existing medical knowledge,
previous empirical findings, and currently dominant neurological
theories. Precisely because the referential quality and the associated
epistemic potential of the resulting images are not self-evident, their
evaluation necessitates a detailed examination of the chains of
operations underlying their production, as performed in this analysis.
Importantly, the chain of operations involved in producing f
maps as epistemic tools in research is not standardized. Instead,
the choice of operations at each step has to be tailored to a
particular research question, meaning that the context-specific
FIG . 4
adequacy of these operations, which can vary immensely across
experiments, needs to be assessed on a case-by-case basis. To
exemplify how the f-based articulation changes when researchers
pose a different question about ’s neural underpinnings, we will
now analyze Barnden et al.’s 2019 study.
Articulating the Deficient Brainstem Connectivity
Whereas Cook et al. (2017) focused on exploring previously unknown
neural effects of ’s major clinical feature, Barnden et al. (2019, 1)
used f to articulate a -related functional brain disturbance
that, as they hypothesized, entailed impaired “nerve signal conduction
through the brainstem.” They derived this hypothesis from previous
structural studies that had indicated such a functional disturbance
in patients based on neuroanatomical findings. As the following
analysis will show, although the articulation comprised the same four
stages discussed above, Barnden et al. performed different
operations at each stage than Cook et al. to accommodate their
specific research question.
To begin with, because their focus was not on ’s clinical
manifestations, Barnden et al. were less concerned with recruiting
patients with stringently delineated symptoms. They selected patients
based only on the broad Fukuda et al. (1994) diagnostic criteria and
did not exclude those with psychiatric comorbidities. Advantageously,
these more lenient criteria allowed them to recruit a significantly
larger sample of 45 patients, who were predominantly but not
exclusively female, thus increasing their findings’ statistical power and
generalizability across genders. The researchers also enrolled 27
healthy control subjects who matched the patients’ age and female-
to-male ratio. The large discrepancy in the group sizes, which
Barnden et al. did not explain, is puzzling and, as we will see later,
presented an obstacle during f analysis.
Next, the researchers defined their f experimental procedure.
Since they aimed to articulate the impaired nerve signal conduction
through a particular brain region, instead of employing the functional
localization approach used by Cook et al., Barnden et al. used the
functional integration approach. As discussed earlier, in the
localization approach, researchers produce f activation maps to
identify the spatial distribution of brain areas activated by a chosen
comparison of experimental conditions. Conversely, in the functional
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integration approach, researchers compute f connectivity maps to
infer how spatially distant brain regions “communicate with one
another and information is passed from one brain area to the next”
(Bijsterbosch, Smith, and Beckmann 2017, 2). The assumption behind
statistical functional connectivity analyses, which underpin the
production of connectivity maps, is that brain regions whose
measured signals correlate over time are functionally connected
(Biswal et al. 1995). Typically, such analyses are associated with the
resting-state f paradigm that investigates spontaneous
fluctuations in brain activity while subjects are not engaged in explicit
tasks. More recently, such analyses have been increasingly applied
in task-based f studies to examine how the interactions between
different brain regions change under different task conditions
(Gerchen and Kirsch 2017).
Notably, Barnden et al. used both resting-state and task-based
f to articulate the brainstem connectivity deficits in patients
compared to healthy controls. Implicit in this decision was the
assumption that the connectivity deficits might manifest differently
during rest versus an external cognitive challenge. In the task-based
segment of their study, Barnden et al. employed a variation of the
well-known Stroop task, involving color words. Participants were
shown word pairs on the monitor and asked to decide if the color of
the upper word matched the meaning of the lower word. Barnden et
al. selected this task “because it tested the attention and
concentration difficulties often reported” by patients (2019, 2).
Thus, as in Cook et al.’s study, the task experimentally addressed
patients’ subjectively experienced disease-related cognitive
difficulties.
During the data collection stage, Barnden et al. generated high-
resolution structural scans of participants, followed by separate
acquisitions of resting-state and task-based f data, each lasting
15 minutes. Compared to Cook et al., they acquired data with
considerably higher spatial and temporal resolutions to increase the
data’s “sensitivity to connectivity changes” (Barnden et al. 2019, 7), a
decision that was aligned with their research question. However, the
resulting high noise level and signal dropout posed challenges during
data processing.
Like Cook et al., Barnden et al. also collected auxiliary non-imaging
data. Throughout f acquisition, participants’ pulse and respiratory
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data were measured. Performance metrics, including response times
and accuracy, were registered during the Stroop task. However, the
study’s focus remained primarily on neuroimaging, with less emphasis
on physiological and behavioral data. Moreover, clinical symptom
characterization was limited to summary scores of participants’
physical and mental health issues obtained using a single self-report
questionnaire. Although potentially more reductive, this approach
aligned with the study’s more technically framed research question.
The fourth stage started with substantial preprocessing of the
resting-state and task-based datasets. Because f is susceptible
to signal distortions in the brainstem during measurement, beyond
applying standard preprocessing steps, such as motion correction,
Barnden et al. developed a tailored normalization procedure. This
operation was required to computationally minimize the significant
signal dropout in the brainstem, without which data would have been
unusable. Additionally, the researchers used the participants’ pulse
and respiratory measurements to further denoise the f data,
reducing the physiological artifacts that might have skewed
subsequent connectivity analyses. Barnden et al. judged this
preprocessing step necessary because physiological noise in f
data is particularly pronounced in the brainstem due to its “proximity
to major cerebral arteries” (2019, 6). Hence, whereas preprocessing
operations were relatively marginal in Cook et al.’s study, due to the
technical challenges of measuring f connectivity in the brainstem,
here these operations acquired major epistemic significance.
After extensive preprocessing, the researcher defined ten
(regions of interest) for connectivity analyses. Aiming to articulate
connectivity deficits within the brainstem, they divided the brainstem
into five , chosen based on earlier structural studies that had
implicated them in neuropathology. To explore if potential intra-
brainstem connectivity deficits affected connectivity disturbances with
other brain regions, Barnden et al. constructed five additional :
one in the cerebellum and four in the bilateral midbrain regions of the
hippocampus and the thalamus. These regions were chosen because
of their “rich connections” with the brainstem (Barnden et al. 2019, 4).
In short, as in Cook et al.’s study, the selection of the was
guided by hypotheses derived from the neuroimaging literature.
To compute the maps, Barnden et al. first submitted their resting-
state and task-based f datasets to separate within-group
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connectivity analyses for patients and healthy controls. However, to
perform statistically valid between-group connectivity analyses, they
first had to address the disbalance in their group sizes. Since
between-group connectivity analyses require equal group sizes for
statistical validity, Barnden et al. excluded 18 subjects from the
patient group. The final between-group resting-state and task-based
connectivity maps were thus computed for equally sized groups and
showed the distribution of functional connectivity deficits in
patients relative to healthy controls. Additionally, Barnden et al.
performed post-hoc analysis to assess the associations between
connectivity and the summary symptom severity for each patient
in their sample, thus exploring if “impaired brainstem connectivity is an
important factor in / aetiology” (2019, 5).
As in Cook et al.’s study, the empirical findings were distributed
across multiple complementary f maps that had to be interpreted
together. The within-group connectivity maps computed separately for
patients and controls indicated statistically significant functional
connections within the brainstem, as well as between the brainstem
and the subcortical nuclei, in each group both during rest and task.
The visual comparison of within-group maps revealed that, in both
groups, more connections were activated during the Stroop task,
when the brainstem “would be actively coordinating cortical arousal
and hemodynamic responses,” than during rest (Barnden et al. 2019,
5). However, the between-group resting-state map was empty. This
negative finding was significant as it suggested the lack of
connectivity differences between patients and healthy controls when
they were not engaged in explicit cognitive tasks. By contrast, the
between-group task-based connectivity map showed reduced
functional connections within the brainstem and between the
brainstem, the hippocampus, and the thalamus in patients relative to
healthy controls. Thus, crucially, their tailor-made chain of operations
that combined resting-state and task-based connectivity approaches
enabled Barnden et al. to articulate patients’ connectivity deficits
that became manifest during active cognitive engagement but not
during rest. Moreover, the post-hoc analyses suggested a correlation
between the impaired brainstem connectivity and the patients’ self-
reported cumulative symptom severity scores, thus linking these
deficits to patients’ experience of illness.
Drawing their image-based empirical findings together, Barnden et
al. posited that the brainstem connectivity deficits their f maps
visualized “can explain autonomic changes and diminish cortical
oscillatory coherence which can impair attention, memory, cognitive
function, sleep quality and muscle tone, all symptoms of /”
(2019, 1). Admittedly, this interpretation remained tentative. However,
it is noteworthy that the novel medical findings about the potential
functional connectivity deficits underpinning , however tentative,
were articulated through f maps that also delivered visual support
for the physical reality of patients’ elusive symptoms. As in Cook et
al.’s study, the viability of the tailor-made empirical images to fulfill the
attributed epistemic functions depended on the quality of the chains of
operations that underpinned their production and served to
establish a referential connection between these images and the
fatigue-related phenomena they visualized.
FIG. 5 Chain of operations underpinning the articulation of connectivity deficits in
in Barnden et al. (2019). Courtesy of Paula Muhr
Summing up, let us highlight the differences in how these two
studies performed the process of articulation. Whereas Cook et al.
aimed to articulate neural correlates of fatigability as ’s defining
clinical feature, Barnden et al. focused on articulating a presumed
-related dysfunctional connectivity within and beyond a particular
brain region. To address their different research questions using f
maps, the researchers lacked standardized image-based solutions.
Instead, they had to develop custom-made chains of operations that
allowed them to generate new empirical findings about through
the mediation of f. As performed by Cook et al., the articulation of
FIG . 5
’s neural basis primarily aimed at reliably linking f and non-
f data derived from the detailed characterization of the exertion-
induced worsening of clinical features in a homogeneously sampled
patient group to the patients’ self-reported experiences of . It
also entailed a careful dissection of ’s neurophysiological and
cognitive components through stratified experimental tasks. By
contrast, the articulation devised by Barnden et al. focused on
disentangling how the hypothesized functional connectivity deficits in
patients varied between demanding cognitive engagement and
rest. Moreover, due to the technological limitations of f, the latter
articulation required the inclusion of multiple operations whose goal
was to computationally mitigate these limitations. The thus produced
f maps made various aspects of ’s neural underpinning
empirically explorable, while also delivering preliminary visual
evidence for the physical reality of this still contested illness.
Overall, this case study has traced how the present-day
deployment of functional neuroimaging technologies in medical
research on has opened new epistemic paths to experimentally
articulating the elusive neurophysiological underpinning of chronic
fatigue. Yet, it demonstrated that while f mediates access to
previously unexplorable aspects of ’s neuropathophysiology, it
also imposes new methodological challenges and limitations that are
not readily apparent in the resulting images. Far from being
straightforward representations of ’s neurobiology, f maps, as
deployed in research, are highly flexible and versatile knowledge-
producing tools whose epistemically pertinent production necessitates
an interlinking of a long series of tailored operations that jointly
determine what will be made visible in the resulting images and what
not. Thus, to understand these images’ epistemic import, we need to
analyze not how these images look but how they were produced and
in which specific contexts.
Conclusion
After a century and a half of medical and physiological research into
its pathology and potential treatments, chronic fatigue remains a
vaguely understood and elusive condition. To make fatigue tangible
and knowable for medical specialists and the wider public, various
visualization practices have been deployed in research and clinical
practices across historical periods. Rather than providing a general
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