Primary headache disorders, such as migraine and cluster headache, are among the most
prevalent and debilitating neurological diseases worldwide (1). An increasing recognition
of the importance of these diseases has led to a growing interest in understanding
their pathophysiology and developing new treatments. From the once popular Vascular
theory that described primary headaches as vascular disorders, the field has now moved
to the Neuronal theories involving either the peripheral or central nervous system,
or both (2). It is now recognized that primary headaches are not simply a disease
of recurrent pain attacks but a complex and multifaceted brain disorder. There is
evidence that in predisposed headache patients various cortical, subcortical, and
brainstem regions are activated, and key neuropeptides are released during the headache
attack (3). Neuroimaging techniques have made a tremendous contribution to our understanding
of headache pathophysiology, providing insights into human brain networks that might
account for the pain and the broad symptomatology characterizing the headache attacks.
The brainstem, including the trigeminovascular pathway, thalamus and hypothalamus
seem to have a pivotal role in triggering the migraine and cluster headache attacks.
Widespread structural and functional alterations in multisensory processing brain
areas have also been shown in both conditions during the interictal and ictal phase
(4).
A better understanding of the mechanisms responsible for the generation of the headache
attacks allows the identification of novel therapeutic targets. In conjunction with
progress in theories of the pathophysiology of primary headaches, the understanding
of the mechanisms of action of acute and preventive treatments for migraine and cluster
headache has evolved. A few neuroimaging studies have explored the therapeutic effects
of pharmacological and non-pharmacological therapeutic approaches commonly used against
migraines and cluster headaches, suggesting a potential central mechanism of action
of these therapies (5–7).
Although much progress has been made in the understanding of migraine and cluster
headache, there are still many unsolved questions to address. Many studies suggested
that brain alterations in headache patients might change dynamically over time, since
they differ according to the headache phase, frequency of attacks, and disease duration
(8, 9). However, some brain alterations are not influenced by the disease activity,
suggesting that they might represent brain biomarkers that predispose to the disease
(10, 11). Further unanswered questions are whether it is possible to identify a specific
neuroimaging pattern for each different headache phenotype and if alterations in the
function and structure of nociceptive brain areas are headache-specific or common
to other chronic pain disorders. Moreover, imaging biomarkers that could predict treatment
response of headache patients are scarce.
A valuable strategy to reduce the unmet needs in the understanding of primary headaches
is to study headache patients using machine learning approaches. These methods have
been employed to study patients with neurological or psychiatric conditions—like Alzheimer's
disease, depression, and chronic pain disorders—in order to identify neuroimaging
biomarkers, which could be used to predict clinical outcomes, including diagnostic
categories, measures of symptoms, prediction of disease evolution, and treatment response
(12, 13). There are two main machine learning approaches: supervised and unsupervised.
Supervised machine learning algorithms are trained to automatically classify individuals
into predefined groups, e.g., patients or healthy controls, and yield an associated
accuracy indicative of how well the model could generalize to future individual cases
(12, 14). At a more detailed level, a machine learning classifier is a function that
takes the values of various features (e.g., different imaging patterns) in an example
and predicts the class that example belongs to (e.g., patient or control). The goal
is to develop a “classifier” that identifies the relation between each example and
its respective category with high accuracy (15). Based on what the algorithm has learned,
it will be then able to classify new, previously unseen data to one of the predefined
categories (12). By contrast, unsupervised machine learning models are data-driven
automated approaches that, without the availability of a priori information supplied
by the operator, seek to classify uncategorized data, with the primary aim of discovering
unknown, but potentially useful information in the data (15). These classification
models include a “training” phase in which training data are used to develop an algorithm
able to discriminate between groups, and a “testing” phase in which the algorithm
is used to blind-predict the group to which a new observation belongs.
The main advantages of using machine learning approaches are that they allow inference
on an individual patient basis and are sensitive to subtle and spatially distributed
patterns of disease-induced changes in the brain that might be undetectable at group
level comparisons (12, 16). The evaluation of the performance of the model in a new
subset of individuals provides valid estimates of how well the discriminative model
generalizes to new data, enhancing the clinical significance of these approaches (16).
Recent machine learning studies have focused on the diagnosis of migraine. Machine
learning algorithms based on brain resting state functional magnetic resonance imaging
(MRI), or morphometric MRI data have been used to identify brain signatures that discriminate
migraine patients from controls (17–19). The functional connectivity of brain regions
involved with processing the affective components of pain, like the insula, amygdala,
temporal, and frontal lobes, discriminated migraine patients from controls with an
accuracy rate of 86%. The discrimination between patients with longer (>14 years)
and shorter (≤ 14 years) disease duration achieved the highest accuracy, suggesting
that disease burden might influence functional reorganization in the brain. The altered
patterns of functional connections that distinguish migraine patients from controls
could represent migraine biomarkers that are further reinforced by recurrent pain
(17). On the other hand, an unsupervised machine learning approach was not able to
clearly separate migraineurs from healthy controls based upon brain morphometric measures
(19). An improvement in classification performance in migraine identification can
be achieved integrating functional and structural imaging metrics that disclose complementary
information regarding the underlying biological processes (20).
A common objection to these studies is that the diagnosis of migraine is mainly based
on taking a good clinical history. However, machine learning studies could be used
to discriminate those headache patients who have challenging clinical presentations,
such as patients with chronic migraine vs. patients with hemicrania continua, patients
with probable migraine vs. tension type-headache or patients with episodic cluster
headache vs. patients with paroxysmal hemicrania. Moreover, given the high prevalence
of migraine, being sure that a control does not harbor migraine biology is very challenging.
In the future, the identification of migraine-specific imaging patterns might improve
the accuracy of the clinical criteria currently used for the diagnosis of migraine.
Supervised and unsupervised machine learning approaches have been applied for migraine
patient stratification. Classifiers containing MRI measures of brain cortical thickness,
cortical surface area, and regional volumes of areas involved in nociception accurately
classified individuals as having chronic migraine, achieving an accuracy of 84% when
compared to episodic migraine patients (18). A data-driven classification study identified
two subgroups of migraine patients based upon their brain structures, with one subgroup
having longer disease duration, higher migraine-related disability and more severe
allodynia symptoms during migraine attacks. Thus, highlighting the role of machine
learning models in identifying migraine patients with different disease courses (19).
Future machine learning studies combining clinical, structural, and functional imaging
measures will be valuable for identifying episodic migraine patients who are at risk
of evolving into a chronic form. These models could provide a basis for early intervention,
which can potentially prevent or even reverse the course of the disease. In the future,
the study of patients with different types of headache and patients with other chronic
pain disorders using machine learning techniques might provide brain “signatures”
that are specific for the different conditions. Thus, providing important information
about the relationships between disorders and symptoms at the biological level.
One of the most promising applications of machine learning techniques lies in their
aid in customizing patients' treatment based on imaging brain fingerprints. Previous
studies have shown the ability of machine learning approaches to predict treatment
response in patients with major depression based on functional and structural brain
imaging patterns (21, 22). It is desirable that future use of machine learning techniques,
imaging, and clinical data, would allow us to identify objective biomarkers that might
facilitate the selection of the most appropriate treatment for each headache patient.
Objective biomarkers that predict treatment response can improve headache patients'
management and reduce unmet treatment needs. Optimized treatments tailored to the
individual patient are essential to improve headache patients' quality of life and
increase patients' productivity.
Despite increasing interest in these emerging techniques, many challenges remain to
be solved. None of the machine learning studies in headache patients have validated
the accuracy of their models in independent datasets, so far. Moreover, the generalizability
of the classification models across different sites and scanners should be evaluated.
Large-scale datasets of headache patients are also needed.
Author Contributions
RM and MF have both contributed to the study concept and drafting and revising the
manuscript.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.