ISSN: 2641-3094
Global Journal of Ecology
Review Article       Open Access      Peer-Reviewed

Asymmetry as an indicator of stress: From population statistics to clinical life-saving applications

Alex Frid1 and Shmuel Raz2*

1Asher Space Research Institute, Technion, Israel
2Department of Information Systems, University of Haifa, Haifa 31905, Israel
*Corresponding author: Shmuel Raz, Department of Information Systems, University of Haifa, Haifa 31905, Israel, E-mail:
Received: 22 November, 2022 | Accepted: 06 January, 2023 | Published: 07 January, 2023

Cite this as

Frid A, Raz S (2023) Asymmetry as an indicator of stress: From population statistics to clinical life-saving applications. Glob J Ecol 8(1): 001-006. DOI: 10.17352/gje.000074


© 2023 Frid A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Most symmetrical objects can be efficiently described in terms of their deviation from a specific symmetry group, whether it be a mirror, radial, or translatory symmetry, among other groups. Fundamentally, asymmetry is an individual trait, but the asymmetry distribution of a given population may provide valuable information about the well-being of that population. Quantification of these deviations from perfect symmetry evolved from counts and linear measures of distances to landmarks conducive to structures with consistent topology, and then to Continuous Symmetry Measures (CSM) conducive to structures with no consistent topology. We demonstrate the usefulness of this approach on quantification of leaf veins that mirror bifurcating structures.

Deviations from a given symmetry group can be described in terms of (i) Fluctuating Asymmetries (FA) or (ii) broken asymmetries. Fluctuating Asymmetry (FA) is a controversial indicator of stress, and therefore tackling the problem needs a large number of species and populations in habitats with well-known stressors. We found such a site at “Evolution Canyon”, Israel, and we examine and discuss a study of twenty-four species that live in the canyon’s opposing slopes.

We conclude with examples from asymmetry as a neurophysiological bioindicator by presenting several studies on Amyotrophic Lateral Sclerosis (ALS), Parkinson’s disease, and stroke. We show how machine-learning methods, applied on asymmetry indicators (in addition to the traditional signal processing features), can improve the sensitivity of the system and provide reliable diagnostic results.


Symmetry is a quality of most objects in the universe, from molecules of hydrogen to galaxies, and across phylogeny, from bacteria to humans. Bilateral, radial, rotational, dihedral, and translational symmetries are present everywhere. These symmetries, however, are never perfect. Deviations from symmetry at the population level may be distributed (in the statistical sense) normally or leptokurtically around a mean of zero (Fluctuating Asymmetry, FA), around a mean different from zero (Directional Asymmetry, DA), or they may display platykurtic or bimodal distributions around a mean of zero (antisymmetry).

Fluctuating Asymmetry (FA) is a measure of developmental noise (random developmental variation) and developmental instability [1-3]. The study of fluctuating asymmetry began with bilateral structures [1-5] and it currently includes diverse symmetries (e.g., radial, translational, complex symmetry, etc.).

The basic measure of fluctuating asymmetry is the variance (or other measure of dispersion) of the studied feature on right and left sides of the symmetry axis, var (r - l), where r is the measure or count on the right side and l is the measure or count on the left side [3,5]. This approach, however, ignores the shape of a structure, and only considers linear distances of morphometric traits or counts of meristic traits. The use of landmarks and continuous symmetry measures enables us to have better insight by taking into account the shape of the structure [6-10].

Fluctuating asymmetry as an indicator of stress at the population level

Fluctuating asymmetry can serve as an indicator of environmental and genetic stress [6,11-17], though its sensitivity is a debatable one [18,19]. The results of fluctuating asymmetry studies are inconsistent-sometimes it increases under environmental and genetic stress [20,21], and at other times it does not [22-24]

Linear asymmetry as a bio-indicator of stress-a microcosm of life’s evolution at evolution canyon: Linear morphometric distances (e.g., distance from the symmetry axis), randomized sampling, and measurements applied to many species from a variety of taxa that share the same habitat, can be used to more precisely evaluate environmental stress and developmental instability [25,26]. We found such an appropriate environment in the Evolution Canyon microsites, where hundreds of studies have been conducted in the last 30 years [27-32] (Figure 1). The opposite slopes of Evolution Canyon are referred to as the “African” south-facing slope and the “European” north-facing slope, and they provide a wonderful opportunity for studying developmental instability in a natural experiment. The opposite slopes of the canyon are separated by 50-100 m at the bottom, have identical geology and soil types, but have different vegetation. The “African” slope is more stressful for many mesic organisms [27-33]. The microclimatic differences cause strong local biodiversity differentiation at all developmental levels (sequences, genes, genomes, populations, species, ecosystems, and biota), and are accompanied by interslope differences in the specie’s richness and abundance [27-31].

Twenty four species belonging to different taxonomic groups were measured [11-13]. The results of these studies are summarized and show (Table 1) that fluctuating asymmetry is negatively correlated with abundance; 17 of the 23 species (74%) displayed higher fluctuating asymmetry on the slope where they were less abundant (p = 0.0347 in binomial test). On the other hand, only 13 of the 23 species showed higher fluctuating asymmetry on the “African” slope (p = 0.54). Our results additionally suggest that the “African” slope is extremely stressful only for species that are on the margins of their adaptive zones. Likewise, the “European” slope is extremely stressful only for species that are on the margins of their different adaptive zones. In these cases, differences in body size represent adaptation rather than the current stress effect.

These results show that a broad approach, encompassing many species (using the same methods), may allow researchers to make generalizations regarding the ability of fluctuating asymmetry to serve as an indicator of stress, and decrease the effect of variability among studies. This is according to Zakharov [34], who suggested a biotest approach, which suggests that the fluctuating asymmetry of several species should be integrated for the study of environmental stress. Graham [35] suggested using meta-analysis when studying several species. However, this approach has been rarely used. Most studies still examine just a single species at a time, and the results are always ambiguous and non-generalizable [35].

Computational modeling of asymmetry of shape using Continuous Symmetry Measurements (CSM)

Three types of objects for quantifying metric asymmetry are given in Figure 2. Landmark methods give the appropriate attention to consistent and partially consistent objects, and continuous symmetry measures extend that attention to inconsistent objects. Consistently symmetrical objects have a consistent topology of specific landmarks that display some form of symmetry. The position of the corresponding landmarks, and the distances among them, may vary (Figure 2A), but their number is maintained. Inconsistent objects have no homologous landmarks, no consistent topology, no quantitative consistency, and sometimes no matching points (Figure 2C).

Landmark methods use developmentally homologous points (i.e., landmarks) for asymmetry analysis, and they represent an advancement of the conventional measurement of distance. Continuous Symmetry Measures (CSM) [10] treat symmetry as a continuous feature rather than a binary feature (i.e., whether it exists or not), and it involves the amount of “effort” required to transform a given shape (composed of orbits) into a symmetric shape. Continuous symmetry measures are an advancement over landmark methods [26] because they can measure deviations from symmetry of objects that have variable structural elements. Hence, they encompass the landmark methods.

Modeling leaf asymmetry using continuous symmetry measure: The leaf vein’s bifurcation structure is an example of a structure that cannot be quantified using landmark methods. Typically, the middle and secondary veins of leaves display planar, tree-like (i.e., bifurcating) geometry. The general layout of the veins consists of a central mid-vein, the symmetry axis from which secondary veins bifurcate on both sides. Individual leaves may vary in the number of secondary veins, their bifurcation points along the mid-vein, their lengths, and their angles. Since no geometric or structural consistency can be assumed, we propose to define matching points based on a biological-specific physiological mechanism, namely the Leaf Venation Hypothesis [36,37] and on the concept of minimum energy [38] (Figure 3).

Milner, et al. [39] developed a way to quantify the amount of asymmetry of one-level bifurcation structure of leaf veins. Since, usually the asymmetry developmentally originates during the early stages of leaf growth; we attempted to quantify the asymmetry introduced during the developmental stages of leaf growth based on the described growth model (Figure 4). Although many factors and chemical substances are involved in the growth process, the model was simplified by considering only the effects on the vein structure of the asymmetries in concentration of auxin in the leaf.

In this approach, the bifurcating structure is viewed as a collection of points interconnected by segments (Figure 5A). The approach requires matching pairs of points to be deformed into symmetric pairs. In the case of our bifurcating structures, this pairing of points reduces to pairing of secondary veins. Figure 5B shows an example where points on paired veins have been matched (point Pk matches point Qk). In the case of a “missing” secondary vein, an insertion of a new vein of zero length is required, which is marked as two endpoints located at the bifurcation point.

The CSM, as shown on bifurcating structures, enables us to study structures that landmarks are not capable of, in a way that is similar to the ability of landmarks methods to study structures that linear measurements are not capable of. The CSM, however, requires identification of the landmarks of the studied structure. Gandhi, et al. [40] used methodologies from information theory in order to find the most appropriate type of symmetry, and to quantify the degree of symmetry using automatic detection of the studied object.

Asymmetry as neurophysiological bioindicator

One of the early symptoms of Neurological Disorders, and more specifically neurodegenerative disorders, is directional asymmetry of the face or the body of the patient. For example, Parkinson’s Disease (PD) and Cerebrovascular Accident (CVA- Stroke) asymmetrically affect the oro-facial musculature with major impairments to speech, swallowing, and oro-motor abilities, as well as expression of emotions [41]. A timely and accurate assessment of oro-facial impairments can contribute to the overall disease diagnosis and lead to early interventions and improved quality of life [42-44].

There are many studies that quantify the body-face morphologies of these asymmetric diseases and disorders. For example, Chang, et al. [45] proposed a facial stroke recognition system, which showed that the proposed system can accurately and effectively distinguish stroke from non-stroke images. Bandini, et al. [46] suggested video-based facial tracking systems that could assist with early diagnosis and progress monitoring of bulbar ALS disease progression. Asymmetrical features can be used also on cortical brain recordings (EEG) to classify even very subtle neurological phenomena such as Dyslexia and ADHD [47]. Symmetry can be also expressed by synchrony of the brain areas [48,49]. Asymmetrical behavior can be spotted also in most fine motor movements such as finger tapping [50,51].

In another study, we quantified the level of stroke rehabilitation based on the Fugl-Meyer Assessment (FMA) [52,53]. This test involves the patient performing specific motor actions. A physician or skilled medical professional rates the performance on the FMA scale, and a subjective score is derived. A novel multi-camera tracking system was applied to evaluate the motor movement of stroke patients (Figure 6).

The subjects performed the Fugl-Meyer assessment, and the FMA scores for the patients and the healthy subjects were estimated. An analysis application was developed for extracting measurements from the tracked body skeleton recordings, as shown in Figure 6, and these measurements were then used to detect correlations with the physician’s diagnosis.

We showed very high classification rates between stroke patients and healthy subjects using our Fugl-Meyer tracking and analysis system [52]. However, the results also showed that classification that used the asymmetry measure yielded inferior results when compared to the other data sets. This, and the fact that the classifications were run on data from both the affected and healthy body sides, implies that both the stroke-affected side and the subject’s healthy side provided distinguishing features, which enabled the separation of patients from healthy subjects.

The rationale behind developing these AI measures is that the state-of-the-art clinical methods are usually subjective and variant to the clinician who performs the evaluation of the existence and progress of the disease. So, there is a need for automatic, non-biased tools.

Currently, the use of artificial intelligence and stroke diagnosis is being coupled for life-saving applications such as the start-up company CVAid Medical, which was co-founded by Doctor Shmuel Raz, a co-author of this review. CVAid Medical was established to perform stroke diagnosis and management by using a smartphone application along with a battery of computer vision and machine learning tools. The CVAid facial recognition system showed an accuracy of 87% in stroke symptom detection, sensitivity 95%, specificity 80%, false negative 5%, false positive 20% [54].

We thank Professor John Graham for comments that greatly improved the manuscript. We thank an anonymous reviewer for his/her inquiries. We are also immensely grateful to Beverly Lowenstein for editing the manuscript.

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