Transcranial Magnetic Stimulation: A New Possibility in Obesity Treatment

All published articles of this journal are available on ScienceDirect.

REVIEW ARTICLE

Transcranial Magnetic Stimulation: A New Possibility in Obesity Treatment

The Open Neurology Journal 30 Sep 2024 REVIEW ARTICLE DOI: 10.2174/011874205X309047240503104533

Abstract

Obesity is a major public health challenge and results from the complex interaction of many etiopathogenetic factors. However, food-related hedonic stimuli and poor inhibitory control often appear to be specific maintenance factors, and conventional treatments are sometimes ineffective. Transcranial magnetic stimulation is emerging as a promising treatment option. Targeting specific brain regions, such as the dorsolateral prefrontal cortex, was found to be effective in modulating acute food craving and improving cognitive control. This review traces the evolution and development of transcranial magnetic stimulation and presents the results of recent randomized clinical trials conducted in obese subjects. These suggest that repetitive transcranial magnetic stimulation and deep transcranial magnetic stimulation may be effective in reducing body weight, BMI and food cravings. The neural circuits involved and the underlying mechanisms of action of this neurostimulation technique are also reviewed. Finally, outstanding questions and future research directions are identified to further understand and develop this promising therapy.

Keywords: Transcranial magnetic stimulation, Obesity, Weight management, Dorsolateral prefrontal cortex, Reword mechanism, Executive functions, Inhibitory control.

1. INTRODUCTION

The increasing prevalence of obesity is a major global public health challenge. Excess body weight significantly increases the risk of a number of chronic diseases, such as type 2 diabetes mellitus, non-alcoholic fatty liver disease, hypertension, and cardiovascular disease, including myocardial infarction and stroke. Obesity is also strongly associated with a wide range of health problems, including osteoporosis, joint disease, renal dysfunction, dyslipidaemia, obstructive sleep apnoea, and certain cancers. It can also lead to musculoskeletal problems and rapid cognitive decline [1-6]. These negative effects underscore the importance of maintaining a healthy weight.

Obesity is a complex condition shaped by a variety of influences, including physiological, metabolic, psycho- logical, and environmental factors [7-10]. The hedonic aspect of eating, characterised by strong cravings and challenges in resisting certain foods, adds another layer of complexity to the problem. In addition, although lifestyle modification, pharmacotherapy, and bariatric surgery offer potential benefits, their effectiveness is limited and may not be appropriate for all individuals [11, 12].

Transcranial magnetic stimulation (TMS), a non-invasive method of modulating brain activity, is emerging as a promising new treatment for obesity and associated eating disorders. TMS targets specific brain regions involved in the regulation of intense food cravings and dietary control and has shown promise in facilitating weight loss [13-16]. Studying obesity through the lens of TMS is challenging due to the complex nature of the disorder and the significant role of neurobehavioral factors [17, 18].

TMS is a potential intervention for modifying neural circuits that are essential for controlling appetite, reducing food cravings, and regulating impulses. These components are closely linked to the development and treatment of obesity [14, 19]. Preliminary studies have shown that TMS has a profound effect on eating behaviour and metabolic functions, opening up new avenues for treatment [20].

These investigations suggest that obesity may result from imbalances in brain networks, with some pathways associated with gratification mechanisms and others involved in cognitive control and impulse inhibition [21-23]. The increasing importance of neural function and control mechanisms in weight management, beyond metabolic or lifestyle factors, is now recognised [17, 18]. In particular, the dorsolateral prefrontal cortex (dlPFC) is a key neural site for modulating hunger and satiety signals and plays an essential role in regulating impulses and food choices [24, 25]. This region has become a specific target of neurostimulation techniques for addictive behaviours and eating disorders because of its role in the executive functions that manage the cognitive regulation of food consumption [26-28]. However, the specific cognitive mechanisms affected by the dlPFC that are modulated by TMS remain largely undefined. It has been theorised that such dynamics may include changes in reward valuation [29], attentional biases [30], or inhibitory control [31]. Whether TMS is effective in reducing food cravings in the long-term has not been conclusively established. Further research is needed to determine the most appropriate use of this technology in the treatment of obesity and related eating disorders, as the current scientific evidence does not provide precise guidelines [32, 33]. Initial research on TMS provides convincing evidence of its therapeutic potential and underlying mechanisms. These findings are a fundamental prerequisite for more in-depth investigations into the use of TMS as an innovative strategy in the treatment of obesity.

This review summarises the current state of knowledge on the use of TMS in the treatment of obesity, examines the efficacy of common TMS techniques in obese subjects enrolled in randomised controlled clinical trials, explores potential mechanisms of action, and highlights areas of uncertainty that require further scientific investigation.

2. NEURAL MODULATION BY TRANSCRANIAL MAGNETIC STIMULATION

TMS employs electromagnetic induction to elicit localized electrical currents within the brain, thereby modulating neuronal activity [34]. Initially developed to treat stubborn psychiatric conditions such as mood disorders, substance abuse, and post-traumatic stress disease [35-39], the application of TMS has subsequently expanded to encompass a range of neurocognitive disorders [40-43] such as schizophrenia [44], dementia [45], and eating disorders [46, 47]. Repetitive Transcranial Magnetic Stimulation (rTMS) and deep Transcranial Magnetic Stimulation (dTMS) constitute the core methodologies within the TMS framework [48-50]. The rTMS protocol employs a figure-of-eight electromagnetic coil to generate focal magnetic pulses or sequences of pulses that target discrete cortical regions to a depth of approximately 1.5 cm below the scalp. These magnetic pulses can modulate cortical excitability, either augmenting or diminishing the electrical activity within the targeted neuronal circuits. Conversely, dTMS is characterised by the use of an H-coil, which is designed to extend stimulation to both cortical and subcortical structures, achieving penetration depths of 4.5 to 5.5 cm from the cranial surface [41, 51-53]. In both rTMS and dTMS modalities, the use of high-frequency stimulation (≥5 Hz) is associated with an excitatory effect on neuronal excitability, whereas low-frequency stimulation (≤1 Hz) is associated with inhibitory effects [30, 54, 55]. Further diversification within TMS techniques includes intermittent theta burst stimulation (iTBS) and continuous theta burst stimulation (cTBS). iTBS, characterised by its high-frequency burst pattern, is postulated to induce an increase in cortical excitability, whereas cTBS is associated with a decrease in cortical excitability [56, 57]. As it has been reported, these stimulation protocols have been found to provide a range of modulatory effects on neural circuits, which highlights the potential usefulness of TMS in the therapeutic modulation of neurocognitive and neuropsychiatric conditions. TMS is highly valued within the clinical paradigm for its ability to induce lasting therapeutic changes beyond the temporal limits of the stimulation sessions themselves [58]. The significance of the neurophysiological changes induced by TMS is highlighted by its capacity to induce long-term synaptic plasticity [59]. Further, rTMS can be classified into single-session and multi-session approaches. The former, a single-session application, is extensively employed in experimental research to evaluate the immediate neurophysiological effects of rTMS. The latter method involves multiple sessions delivered over consecutive days and is primarily used in clinical settings for extended therapeutic interventions [34].

The efficacy and specificity of TMS interventions depend on the precise localization of the targeted brain region. Neuroimaging techniques, such as electro- encephalography (EEG) or structural magnetic resonance imaging (MRI), are necessary for this purpose. These techniques should be complemented by neuronavigation technologies, a methodology that has been extensively documented in the literature [60, 61]. Precision in targeting is essential to optimize the therapeutic outcome of TMS. TMS is considered a safe and non-invasive method that is generally well-tolerated [62, 63]. However, it has been reported that the incidence of side effects was approximately 5%. The most common were headache (46%), lightheadedness (22%), muscle twitching (10%), and a general feeling of lightheadedness (10%) [64]. It is important to note that the main safety concern with TMS is epileptic seizures, although these are very rare and may only be a risk for those with a pre-existing epileptic condition [62-64].

3. TRANSCRANIAL MAGNETIC STIMULATION FOR THE TREATMENT OF OBESITY: A SYNTHESIS OF RANDOMIZED CONTROLLED TRIALS

In line with the latest scientific literature [27, 65, 66], this section will provide an overview of studies investigating the effects of TMS in obese individuals. The focus will be on two specific TMS techniques, rTMS and dTMS. This chapter examines rigorous Randomized Controlled Trials (RCTs) comparing active neuro- modulation techniques with sham interventions to determine the impact of such stimulations on key indicators of interest in the field of obesity research. The particular metrics being monitored are body weight, body mass index (BMI), and cravings for food.

3.1. Repetitive Transcranial Magnetic Stimulation and Obesity

According to a study conducted by Kim and colleagues in 2018 [32], 57 individuals between the ages of 18 and 65 were randomly assigned to two groups in a two-week, single-blind trial. Of the participants, 29 received rTMS, while 28 received sham treatment. The rTMS sessions, each lasting 20 minutes at a frequency of 10 Hz, targeted the left dlPFC. The study results indicated significant weight loss in the rTMS group, accompanied by reductions in BMI, visceral fat, and calorie intake.

Continuing from the previous study, the research group [67] conducted a four-week study on 43 patients who were classified as obese and aged between 18 and 70. The participants were divided into two groups: 21 individuals received eight 20-minute sessions of rTMS at 10 Hz, while the remaining 22 individuals received sham treatments. The study results indicate that individuals who received rTMS treatment experienced a significantly greater weight loss (2.75 kg, SD 2.37) compared to those who received the sham treatment (0.38 kg, SD 1.0). Furthermore, the rTMS group also showed significant reductions in fat mass and visceral adipose tissue by the fourth week. Additionally, after treatment, the rTMS group demonstrated reduced daily kilocalorie and carbohydrate consumption compared to the control group.

In a study conducted in 2019, the effects of combining rTMS with a low-carbohydrate diet were tested on 37 overweight or obese patients [15]. The participants were randomly assigned to two groups: 18 followed the diet with rTMS, and 19 followed the diet with sham rTMS. After 17 sessions of 10 Hz rTMS to the left dlPFC, the treatment group showed significant reductions in body weight and food cravings, as well as improvements in anxiety symptoms, physical functionality, and body image.

In 2020, a study was conducted on 29 obese Filipino patients aged between 15 and 65. The patients were randomly assigned to either a treatment group (15 subjects) or a sham group (14 subjects). They received four 20-minute rTMS sessions at 10 Hz to the left dlPFC over two weeks in a single-blind, monocentric setting [13]. Upon completion of the study, the treatment group exhibited a significant decrease in BMI (-0.6, SD 0.6) and body weight (-1.3 kg, SD 1.3). It is worth noting that the weight reduction did not appear to be sustained beyond the 6-12 week treatment period.

3.2. Deep Transcranial Magnetic Stimulation and Obesity

A pilot study was conducted on 33 obese individuals (9 men, 24 women, mean age 48.1 years, SD 10.6) [33]. The participants were divided into three groups: 13 underwent a 5-week high-frequency dTMS treatment (18 Hz; HF group), 10 received low-frequency dTMS (1 Hz; LF group), and 10 received placebo treatments (sham group). The stimulation targeted the bilateral Prefrontal Cortex (PFC) and Insula, consisting of 15 sessions, each lasting 30 minutes. Food cravings, metabolic indicators, and neuroendocrine measures were assessed at baseline, after 5 weeks of treatment, and at follow-up sessions (1 month, 6 months, and 1 year post-treatment). The findings showed a significant decrease in both body weight (-7.83 kg, SD 2.28) and BMI (-2.83, SD 0.83) in the HF group compared to the sham group. Additionally, there was a significant trend towards reduced food cravings in the HF group compared to the LF and sham groups. The HF group also demonstrated significant improvements in metabolic variables and physical activity.

In another study involving 22 obese individuals (17 female; mean age 44.9 ± 2.2 years; BMI 37.5 ± 1.0 kg/m2) [68], the same researchers suggested that dTMS may have the potential to influence both the pathways of the brain-gut communication and the composition of the gut microbiome. The study involved randomising participants into three groups, each attending 30-minute sessions three times a week for five weeks. They received either high-frequency (18 Hz - HF), low-frequency (1 Hz - LF), or sham dTMS treatments, respectively, with the stimulation site being the bilateral PFC and Insula. After 5 weeks, the HF group showed significant weight loss compared to the LF and sham groups (HF: -4.1 kg, SD 0.8 vs. LF: -1.9 kg, SD 0.8 vs. sham: -1.3 kg, SD 0.6). Moreover, it appears that HF dTMS treatment has had a positive impact on the gut microbiota composition, reversing previous changes and promoting bacterial species with anti-inflammatory properties.

A preliminary randomized, double-blind, placebo-controlled study [14] was conducted to investigate the resting-state functional connectivity (rsFC) in obese patients after 15 sessions of 30-minute treatments. Nine participants underwent high-frequency (18 Hz - HF) dTMS treatment, focusing on the bilateral PFC and Insula, three times a week for five weeks, while 8 were given placebo treatments (sham TMS group). Out of the 17 participants, 6 were diagnosed with Type 2 Diabetes (T2D). The results indicate that the experimental group experienced a significant decrease in body weight and BMI, which persisted through a one-month follow-up. Additionally, there was an observed enhancement in the functional brain connectivity within the medial Orbitofrontal Cortex (mOFC), coupled with a reduction in connectivity with the occipital pole. These findings indicate a potential brain mechanism behind weight loss, characterized by diminished responsiveness to bottom-up visual sensory inputs and an increased dependence on top-down cognitive decision-making processes.

A study was conducted to examine the correlation between psychological symptoms and neuroendocrine parameters in individuals with obesity [69]. The study also investigated the effects of a 5-week treatment regimen involving 30-minute high-frequency (18 Hz) stimulations targeting the bilateral PFC and Insula using dTMS. A study was conducted on 45 patients who were obese, out of which 33 were female. The patients had an average age of 48.8 years (SD 9.9), body weight of 97.6 kg (SD 14.2), and BMI of 36.2 (SD 4.2). The patients were randomly assigned to two groups, out of which 26 patients received high-frequency (HF) dTMS, while 19 underwent sham stimulation. The study found that the HF group showed a significant reduction in body weight and BMI, along with a decrease in impulsivity levels. Additionally, a positive correlation was observed between decreased impulsivity and leptin levels. These results indicate that dTMS was effective in reducing both BMI and impulsivity, improving inhibitory control of the PFC, and impacting the neuroendocrine system, particularly with regard to leptin.

4. MECHANISMS OF ACTION OF TMS IN THE TREATMENT OF OBESITY

These studies suggest that both TMS methods are effective in reducing body weight and BMI, with high-frequency stimulation of the dlPFC showing particular promise, which is in line with previous research [18, 70-72]. However, further research is required to investigate the effects on food cravings [27]. The current evidence suggests that by reducing the frequency and intensity of food cravings, it may be possible to decrease calorie intake and facilitate fat loss [32, 67]. In other words, by strengthening cognitive regulatory competencies, individuals may be better equipped to exercise discipline in their dietary practices. This enhancement provides the opportunity to choose healthier food options instead of those prompted by impulsive or emotional consumption patterns, which can aid in the weight loss process [73-77]. The phenomenon of craving, characterized as an intense and uncontrollable urge to consume, is believed to be influenced by dysfunction in frontostriatal brain circuits that are involved in both substance abuse and overeating [78]. High-frequency rTMS can selectively activate dlPFC while reducing activity in deeper regions such as the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) [79, 80]. However, the effects of TMS on food cravings appear to be a topic of debate, with some studies highlighting the ambiguity of treatment responses. A study was conducted to evaluate the effectiveness of high-frequency rTMS targeting the left dlPFC in reducing food cravings in a group of 28 female participants [81]. Both real and sham rTMS sessions were administered before and after participants were exposed to highly palatable foods. The results indicated that self-reported craving remained unchanged after the real rTMS treatment but increased in the sham condition. However, it should be noted that no significant variations in snack consumption were observed during the brief 5-minute post-stimulation period, regardless of the type of rTMS administered. In another study, the effect of rTMS on the left PFC in 10 healthy women was investigated using an improved sham condition 82]. The participants were randomly allocated to one of two groups and were unaware of which treatment they were receiving. Both conditions demonstrated a noteworthy decrease in craving, with no significant difference observed between real and sham rTMS, even after taking into account the time elapsed since the last meal. However, it was found that prefrontal rTMS did not prove to be superior to the sham condition in reducing craving. It has been suggested [78] that the mild discomfort induced by both real and simulated rTMS may contribute to the reduction in food cravings, suggesting that this reduction may not be solely due to the specific effects of rTMS. Further research is necessary to establish whether the decrease in food cravings caused by rTMS is directly affected by the uncomfortable sensation it produces or if it occurs through an indirect mechanism. Craving is an adaptive mechanism that signals the organism's nutritional needs. It has been considered essential for human survival, especially in an evolutionary context, as it facilitated the accumulation of food supplies in anticipation of times of scarcity [83]. For example, a growing appetite for iron-rich foods, such as meat, has been documented in response to shortages of this essential nutrient [84]. However, it is worth noting that the widespread availability of sugary and fatty foods has transformed these previously adaptive cravings into potential contributors to the development of obesity or uncontrollable eating behaviour [85]. There is a growing body of evidence to suggest that cravings are a key factor in the obesity epidemic [86-88]. Some studies indicate a correlation between obesity, binge eating behaviour, and self-control issues. According to research, a lack of control and an increased desire for food, especially palatable or high-calorie foods, may result in a loss of control over food intake, leading to weight gain. Additionally, studies have indicated that higher food cravings are linked not only to increased body weight but also to lower success rates in weight loss programmes [89-91]. According to experimental studies, there may be common neuro- biological bases between addiction and morbid obesity, particularly in the phenomenon of craving [92]. It has been observed that certain regions of the brain, such as the OFC, are involved in both food craving and addiction control. The OFC plays an essential role in assessing the rewarding properties of stimuli, which suggests that both food and substances can activate it in a similar way. Furthermore, it should be noted that there appears to be a correlation between heightened activation of the OFC and increased food cravings in individuals with normal weight [93]. However, the full impact of brain stimulation on food cravings is not yet fully understood. As previously described [65], there are various potential mechanisms that could be responsible, such as improved cognitive control, alterations in reward perception, or heightened dopaminergic activity. The role of the dlPFC is particularly intriguing in this context. It has been suggested that reduced activity in the dlPFC may contribute to weight gain, as it has been linked to satiety and craving [94, 95]. On the other hand, stimulation of the dlPFC has been shown to enhance cognitive control and effectively suppress the compulsive urge to eat [96]. Furthermore, research has indicated that the interaction between the dlPFC and mOFC may affect the evaluation of food stimuli, leading to a reduction in attractiveness and more regulated food choices [29]. The correlation between improved inhibitory control and reduced cravings suggests that neuromodulation may be able to enhance brain networks involved in behavioural food control by increasing the ability to resist food-related stimuli [97]. According to research, it has been found that the stimulation of the dlPFC can enhance cognitive control and reduce food cravings [98]. It has been observed that the dlPFC, OFC, and ACC together form the executive control network that is critical for desire management and decision-making. Additionally, these regions interact with the orexinergic system [99-103]. Another plausible hypothesis is that neurostimulation of the dlPFC may stimulate dopamine production in the corpus striatum [104]. It is suggested that dopamine levels may increase either directly, through corticostriatal projections, or indirectly, through cortical projections to mesostriatal dopamine neurons located in the midbrain [105-107]. Research has indicated that PFC stimulation in animal studies can activate both the striatum and Ventral Tegmental Areas (VTA), suggesting that both pathways are sensitive to neurostimulation [108, 109]. Additionally, dTMS has been shown to rebalance the dopamine-cortisol ratio during alcohol withdrawal [110]. It is well established that dopamine plays a significant role in inhibitory control, and abnormalities in this area can lead to behavioural disorders such as obesity [111]. Indeed, a correlation has been found between BMI and a decrease in the availability of dopamine D2 receptors [112]. This could potentially lead to pathological eating behaviour as the brain attempts to compensate for reduced activity in motivational and reward circuits. Hence, it may be suggested that individuals who experience intense food cravings and/or obesity could potentially benefit from modifying their dopamine levels through non-invasive brain modulation techniques. Moreover, it has been shown that dTMS is associated with changes in leptin levels and behavioural impulsivity [69]. Leptin, a hormone produced by adipocytes and intestinal enterocytes, plays a crucial role in regulating energy homeostasis [113]. The regulation of leptin levels may have a positive impact on appetite and food intake, while leptin resistance may be linked to increased food intake and the onset of obesity [114]. Specifically, a 5-week course of radiofrequency dTMS treatment led to significant changes in the gut microbiome composition of obese subjects [68]. These changes helped to normalize the microbiota, bringing it closer to that found in normal-weight subjects, and also favored an increase in bacterial species with anti-inflammatory properties. These findings suggest that this intervention may have therapeutic potential in the treatment of obesity. Although there are many theoretical insights to explain the effects of neurostimulation on weight loss, identifying the exact mechanisms remains a challenge.

CONCLUSION AND FUTURE DIRECTION

This review examines the potential of TMS as a new approach to treating obesity. The review suggests that non-invasive neuromodulation may be an effective standalone treatment or may improve therapeutic outcomes when used in combination with other strategies, such as diet. The theoretical basis for this approach is supported by experimental results that demonstrate the complex interplay between neurophysiology and obesity. It proposes a paradigm shift towards specific interventions aimed at modulating and normalising neural circuits using advanced neuromodulation techniques [20, 93, 100, 115-117]. However, it may be beneficial for future research to address the limitations and unresolved questions surrounding the potential applications of neuromodulation in eating behaviour. To gain a more complete and accurate understanding of the role of TMS in dysfunctional eating behaviour, researchers could consider focusing on the following key aspects.

In experimental neuromodulation research, it is essential to enhance study blinding. It is recommended that participants remain unaware of the treatment they are receiving, whether real or sham, to prevent any potential bias in their behaviour or responses. It has been observed in some neuromodulation studies that participants were able to correctly identify experimental conditions with 79% accuracy [82, 118]. Therefore, it is suggested that future research should explore the use of parallel methods to enhance blinding.

Another important aspect is to consider using more meaningful outcome measures. While some studies rely on self-report measures directly reported by participants, others use weight indices that may be outdated, making their true clinical relevance uncertain. For example, the BMI has been criticised for its inability to distinguish between muscle mass and fat mass, as well as for its inability to measure regional adiposity [119-121]. In contrast, it could be argued that waist circumference is a more sensitive measure of visceral adiposity [122-124].

Further research is needed to fully understand the interaction between neuromodulation and gender. Neuropsychological evidence suggests that gender may have a significant impact on prefrontal executive performance [125]. It has been observed that men and women exhibit different abilities in specific subdomains, such as attention, planning, inhibition, and verbal fluency [126-132]. However, it is important to note that these differences do not necessarily imply systematic differences between the sexes. Instead, they reflect differences in the cognitive strategies employed during cognitive tasks [133]. These differences could be due to variability in the anatomic-functional characteristics of the brain and the involvement of neurotransmitter systems, including dopamine and serotonin [134-141].

Moreover, when studying feeding processes and mechanisms, it is crucial to take into account the fluctuations in brain activity that may be associated with metabolic conditions. Therefore, it is recommended that studies explicitly state the time since the last meal and its effect. The review reports that hunger and satiety conditions may produce significant differences in hormonal and neurotransmitter systems. Similarly, when considering the effects of neuromodulation, it is important to take into account the individual's previous or current diets, including their duration and any possible relapses [143]. It is worth noting that individuals who suffer from eating disorders and obesity often follow strict diets, which can significantly affect brain excitability and responsiveness to neuromodulation. Additionally, it is crucial to report whether participants are currently losing weight or maintaining a stable weight. The passage presents data that has implications for both the brain's resting state and its response to neuromodulation [20]. Furthermore, it is worth noting that an individual’s unique anatomical features may affect the propagation of electromagnetic signals [143]. Therefore, it is important to investigate the impact of intracranial adipose tissue on current density distribution, as adipose tissue is known to be more resistive [63, 144].

As shown in this review, TMS offers a promising perspective for the study and treatment of neural vulnerabilities associated with obesity. However, while neurostimulation techniques were originally developed to address the lack of effective treatments in neurology and psychiatry, the application of neuromodulation to the modification of eating behaviour is a more recent development in the field. TMS has been shown to produce temporary and long-term changes by actively affecting the strength of synaptic connections. The primary target of TMS is the dlPFC, a complex brain region associated with executive function and cognitive control of food intake. It can influence the balance between craving and the ability to exercise cognitive control, potentially reducing the rewarding mechanisms that drive excessive eating. It is worth noting that research in the area of actively manipulating the human brain is still in its early stages, and no definitive conclusions are available. Moreover, it is essential to bear in mind that transcranial devices should not be treated as playthings and must be used responsibly [143].

LIST OF ABBREVIATIONS

TMS = Transcranial magnetic stimulation
dlPFC = Dorsolateral prefrontal cortex
rTMS = Repetitive Transcranial Magnetic Stimulation
dTMS = Deep Transcranial Magnetic Stimulation
iTBS = Intermittent theta burst stimulation
cTBS = Continuous theta burst stimulation
EEG = Electroencephalography
MRI = Structural magnetic resonance imaging
RCTs = Randomized Controlled Trials
BMI = Body mass index
HF = High Frequency
LF = Low frequency
PFC = Prefrontal Cortex
rsFC = Resting-state functional connectivity
T2D = Type 2 Diabetes
mOFC = Medial Orbitofrontal Cortex
OFC = Orbitofrontal cortex
ACC = Anterior cingulate cortex
VTA = Ventral Tegmental Areas

CONSENT FOR PUBLICATION

Not applicable.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

REFERENCES

1
Blüher M. Obesity: Global epidemiology and pathogenesis. Nat Rev Endocrinol 2019; 15(5): 288-98.
2
Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000; 894: i-xii, 1-253.
3
Abdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Ann Transl Med 2017; 5(7): 161-1.
4
Dye L, Boyle NB, Champ C, Lawton C. The relationship between obesity and cognitive health and decline. Proc Nutr Soc 2017; 76(4): 443-54.
5
Leigh SJ, Morris MJ. Diet, inflammation and the gut microbiome: Mechanisms for obesity-associated cognitive impairment. Biochim Biophys Acta Mol Basis Dis 2020; 1866(6): 165767.
6
Monda V, La Marra M, Perrella R, et al. Obesity and brain illness: From cognitive and psychological evidences to obesity paradox. Diabetes Metab Syndr Obes 2017; 10: 473-9.
7
Wright SM, Aronne LJ. Causes of obesity. Abdom Radiol 2012; 37(5): 730-2.
8
Aronne LJ, Nelinson DS, Lillo JL. Obesity as a disease state: A new paradigm for diagnosis and treatment. Clin Cornerstone 2009; 9(4): 9-29.
9
Di Maio G, Alessio N, Demirsoy IH, et al. Evaluation of browning agents on the white adipogenesis of bone marrow mesenchymal stromal cells: A contribution to fighting obesity. Cells 2021; 10(2): 403.
10
Di Maio G, Alessio N, Peluso G, Perrotta S, Monda M, Di Bernardo G. Molecular and physiological effects of browning agents on white adipocytes from bone marrow mesenchymal stromal cells. Int J Mol Sci 2022; 23(20): 12151.
11
Jeffery RW, Epstein LH, Wilson GT, et al. Long-term maintenance of weight loss: Current status. Health Psychol 2000; 19(1, Suppl): 5-16.
12
Colquitt JL, Pickett K, Loveman E, Frampton GK. Surgery for weight loss in adults. Cochrane Libr 2014; 2014(10): CD003641.
13
Encarnacion M, Dampil OA, Damian L, Doquenia ML, Redondo-Samin DC, Woolbright MK. Efficacy of repetitive transcranial magnetic stimulation (rTMS) in inducing weight loss among obese filipino patients: A randomized controlled trial. J ASEAN Fed Endocr Soc 2020; 35(2): 181-9.
14
Devoto F, Ferrulli A, Zapparoli L, et al. Repetitive deep TMS for the reduction of body weight: Bimodal effect on the functional brain connectivity in “diabesity”. Nutr Metab Cardiovasc Dis 2021; 31(6): 1860-70.
15
Alvarado-Reynoso B, Ambriz-Tututi M. Effects of repetitive transcranial magnetic stimulation in combination with a low-carbohydrate diet in overweight or obese patients. A randomized controlled trial. Obes Med 2019; 14: 100095.
16
Burgess EE, Sylvester MD, Morse KE, et al. Effects of transcranial direct current stimulation (tDCS) on binge‐eating disorder. Int J Eat Disord 2016; 49(10): 930-6.
17
Berthoud HR, Morrison C. The brain, appetite, and obesity. Annu Rev Psychol 2008; 59(1): 55-92.
18
Robles B, Kuo T, Galván A. Understanding the neuroscience underpinnings of obesity and depression: Implications for policy development and public health practice. Front Public Health 2021; 9: 714236.
19
McFadden KL, Cornier MA, Melanson EL, Bechtell JL, Tregellas JR. Effects of exercise on resting-state default mode and salience network activity in overweight/obese adults. Neuroreport 2013; 24(15): 866-71.
20
Alonso-Alonso M, Pascual-Leone A. The right brain hypothesis for obesity. JAMA 2007; 297(16): 1819-22.
21
Dalton B, Bartholdy S, McClelland J, et al. Randomised controlled feasibility trial of real versus sham repetitive transcranial magnetic stimulation treatment in adults with severe and enduring anorexia nervosa: the TIARA study. BMJ Open 2018; 8(7): e021531.
22
O’Hara CB, Campbell IC, Schmidt U. A reward-centred model of anorexia nervosa: A focussed narrative review of the neurological and psychophysiological literature. Neurosci Biobehav Rev 2015; 52: 131-52.
23
Wierenga CE, Ely A, Bischoff-Grethe A, Bailer UF, Simmons AN, Kaye WH. Are extremes of consumption in eating disorders related to an altered balance between reward and inhibition? Front Behav Neurosci 2014; 8: 410.
24
La Marra M, Ilardi CR, Villano I, et al. Functional relationship between inhibitory control, cognitive flexibility, psychomotor speed and obesity. Brain Sci 2022; 12(8): 1080.
25
La Marra M, Villano I, Ilardi CR, et al. Executive functions in overweight and obese treatment-seeking patients: Cross-sectional data and longitudinal perspectives. Brain Sci 2022; 12(6): 777.
26
La Marra M, Ilardi CR, Villano I, et al. Higher general executive functions predicts lower body mass index by mitigating avoidance behaviors. Front Endocrinol 2022; 13: 1048363.
27
Song S, Zilverstand A, Gui W, Li H, Zhou X. Effects of single-session versus multi-session non-invasive brain stimulation on craving and consumption in individuals with drug addiction, eating disorders or obesity: A meta-analysis. Brain Stimul 2019; 12(3): 606-18.
28
Hanlon CA, Dowdle LT, Austelle CW, et al. What goes up, can come down: Novel brain stimulation paradigms may attenuate craving and craving-related neural circuitry in substance dependent individuals. Brain Res 2015; 1628(Pt A): 199-209.
29
Camus M, Halelamien N, Plassmann H, et al. Repetitive transcranial magnetic stimulation over the right dorsolateral prefrontal cortex decreases valuations during food choices. Eur J Neurosci 2009; 30(10): 1980-8.
30
Fregni F, Orsati F, Pedrosa W, et al. Transcranial direct current stimulation of the prefrontal cortex modulates the desire for specific foods. Appetite 2008; 51(1): 34-41.
31
Lapenta OM, Sierve KD, de Macedo EC, Fregni F, Boggio PS. Transcranial direct current stimulation modulates ERP-indexed inhibitory control and reduces food consumption. Appetite 2014; 83: 42-8.
32
Kim SH, Chung JH, Kim TH, et al. The effects of repetitive transcranial magnetic stimulation on eating behaviors and body weight in obesity: A randomized controlled study. Brain Stimul 2018; 11(3): 528-35.
33
Ferrulli A, Macrì C, Terruzzi I, et al. Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double‐blind, sham‐controlled study. Diabetes Obes Metab 2019; 21(8): 1849-60.
34
Hall PA, Vincent CM, Burhan AM. Non-invasive brain stimulation for food cravings, consumption, and disorders of eating: A review of methods, findings and controversies. Appetite 2018; 124: 78-88.
35
McGirr A, Karmani S, Arsappa R, et al. Clinical efficacy and safety of repetitive transcranial magnetic stimulation in acute bipolar depression. World Psychiatry 2016; 15(1): 85-6.
36
Kim DR, Pesiridou A, O’Reardon JP. Transcranial magnetic stimulation in the treatment of psychiatric disorders. Curr Psychiatry Rep 2009; 11(6): 447-52.
37
Berlim MT, van den Eynde F, Tovar-Perdomo S, Daskalakis ZJ. Response, remission and drop-out rates following high-frequency repetitive transcranial magnetic stimulation (rTMS) for treating major depression: A systematic review and meta-analysis of randomized, double-blind and sham-controlled trials. Psychol Med 2014; 44(2): 225-39.
38
Brunoni AR, Chaimani A, Moffa AH, et al. Repetitive transcranial magnetic stimulation for the acute treatment of major depressive episodes. JAMA Psychiatry 2017; 74(2): 143-52.
39
Couturier JL. Efficacy of rapid-rate repetitive transcranial magnetic stimulation in the treatment of depression: A systematic review and meta-analysis. J Psychiatry Neurosci 2005; 30(2): 83-90.
40
Lefaucheur JP, Aleman A, Baeken C, et al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018). Clin Neurophysiol 2020; 131(2): 474-528.
41
Bersani FS, Minichino A, Enticott PG, et al. Deep transcranial magnetic stimulation as a treatment for psychiatric disorders: A comprehensive review. Eur Psychiatry 2013; 28(1): 30-9.
42
Isserles M, Shalev AY, Roth Y, et al. Effectiveness of deep transcranial magnetic stimulation combined with a brief exposure procedure in post-traumatic stress disorder--a pilot study. Brain Stimul 2013; 6(3): 377-83.
43
Minichino A. ECT, rTMS, and deepTMS in pharmacoresistant drug-free patients with unipolar depression: A comparative review. Neuropsychiatr Dis Treat 2012; 8: 55-64.
44
Cole JC, Green Bernacki C, Helmer A, Pinninti N, O’reardon JP. Efficacy of transcranial magnetic stimulation (TMS) in the treatment of schizophrenia: A review of the literature to date. Innov Clin Neurosci 2015; 12(7-8): 12-9.
45
Elder GJ, Taylor JP. Transcranial magnetic stimulation and transcranial direct current stimulation: Treatments for cognitive and neuropsychiatric symptoms in the neurodegenerative dementias? Alzheimers Res Ther 2014; 6(5-8): 74.
46
Sauvaget A, Trojak B, Bulteau S, et al. Transcranial direct current stimulation (tDCS) in behavioral and food addiction: A systematic review of efficacy, technical, and methodological issues. Front Neurosci 2015; 9: 349.
47
McClelland J, Dalton B, Kekic M, Bartholdy S, Campbell IC, Schmidt U. A systematic review of temporal discounting in eating disorders and obesity: Behavioural and neuroimaging findings. Neurosci Biobehav Rev 2016; 71: 506-28.
48
Walsh V, Cowey A. Transcranial magnetic stimulation and cognitive neuroscience. Nat Rev Neurosci 2000; 1(1): 73-80.
49
Ekhtiari H, Tavakoli H, Addolorato G, et al. Transcranial electrical and magnetic stimulation (tES and TMS) for addiction medicine: A consensus paper on the present state of the science and the road ahead. Neurosci Biobehav Rev 2019; 104: 118-40.
50
Spagnolo PA, Goldman D. Neuromodulation interventions for addictive disorders: Challenges, promise, and roadmap for future research. Brain 2017; 140(5): aww284.
51
Levkovitz Y, Roth Y, Harel EV, Braw Y, Sheer A, Zangen A. A randomized controlled feasibility and safety study of deep transcranial magnetic stimulation. Clin Neurophysiol 2007; 118(12): 2730-44.
52
Zangen A, Roth Y, Voller B, Hallett M. Transcranial magnetic stimulation of deep brain regions: Evidence for efficacy of the H-Coil. Clin Neurophysiol 2005; 116(4): 775-9.
53
Barker AT. The history and basic principles of magnetic nerve stimulation. Electroencephalogr Clin Neurophysiol Suppl 1999; 51: 3-21.
54
Peterchev AV, Wagner TA, Miranda PC, et al. Fundamentals of transcranial electric and magnetic stimulation dose: Definition, selection, and reporting practices. Brain Stimul 2012; 5(4): 435-53.
55
Siebner H, Rothwell J. Transcranial magnetic stimulation: New insights into representational cortical plasticity. Exp Brain Res 2003; 148(1): 1-16.
56
Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC. Theta burst stimulation of the human motor cortex. Neuron 2005; 45(2): 201-6.
57
Di Lazzaro V, Pilato F, Dileone M, et al. The physiological basis of the effects of intermittent theta burst stimulation of the human motor cortex. J Physiol 2008; 586(16): 3871-9.
58
Gangitano M, Valero-Cabré A, Tormos JM, Mottaghy FM, Romero JR, Pascual-Leone Á. Modulation of input–output curves by low and high frequency repetitive transcranial magnetic stimulation of the motor cortex. Clin Neurophysiol 2002; 113(8): 1249-57.
59
Ziemann U. TMS induced plasticity in human cortex. Rev Neurosci 2004; 15(4): 253-66.
60
Dayan E, Censor N, Buch ER, Sandrini M, Cohen LG. Noninvasive brain stimulation: From physiology to network dynamics and back. Nat Neurosci 2013; 16(7): 838-44.
61
Fröhlich F. Network neuroscience 2016.
62
Rossi S, Hallett M, Rossini PM, Pascual-Leone A. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 2009; 120(12): 2008-39.
63
Nitsche MA, Cohen LG, Wassermann EM, et al. Transcranial direct current stimulation: State of the art 2008. Brain Stimul 2008; 1(3): 206-23.
64
Oberman L, Edwards D, Eldaief M, Pascual-Leone A. Safety of theta burst transcranial magnetic stimulation: A systematic review of the literature. J Clin Neurophysiol 2011; 28(1): 67-74.
65
Lowe CJ, Vincent C, Hall PA. Effects of noninvasive brain stimulation on food cravings and consumption: A meta-analytic review. Psychosom Med 2017; 79(1): 2-13.
66
Alhindi YA, Khalifa N, Al-Khyatt W, Idris I. The use of non‐invasive brain stimulation techniques to reduce body weight and food cravings: A systematic review and meta‐analysis. Clin Obes 2023; 13(6): e12611.
67
Kim SH, Chung J, Kim TH, et al. The effects of repetitive transcranial magnetic stimulation on body weight and food consumption in obese adults: A randomized controlled study. Brain Stimul 2019; 12(6): 1556-64.
68
Ferrulli A, Drago L, Gandini S, et al. Deep transcranial magnetic stimulation affects gut microbiota composition in obesity: Results of randomized clinical trial. Int J Mol Sci 2021; 22(9): 4692.
69
Luzi L, Gandini S, Massarini S, et al. Reduction of impulsivity in patients receiving deep transcranial magnetic stimulation treatment for obesity. Endocrine 2021; 74(3): 559-70.
70
Hardee JE, Phaneuf C, Cope L, Zucker R, Gearhardt A, Heitzeg M. Neural correlates of inhibitory control in youth with symptoms of food addiction. Appetite 2020; 148: 104578.
71
Cardinali DP. Fourth Level: The Limbic System Autonomic Nervous System 2018; 245-85.
72
Oliva R, Morys F, Horstmann A, Castiello U, Begliomini C. The impulsive brain: Neural underpinnings of binge eating behavior in normal-weight adults. Appetite 2019; 136: 33-49.
73
Polito R, Scarinci A, Ambrosi A, et al. The beneficial effects of physical activity and weight loss on human colorectal carcinoma cell lines. J Hum Sport Exerc 2020; 15: S252-60.
74
La Marra M, Messina A, Ilardi CR, et al. Factorial model of obese adolescents: The role of body image concerns and selective depersonalization—a pilot study. Int J Environ Res Public Health 2022; 19(18): 11501.
75
Villano I, Ilardi CR, Arena S, et al. Obese subjects without eating disorders experience binge episodes also independently of emotional eating and personality traits among university students of Southern Italy. Brain Sci 2021; 11(9): 1145.
76
Villano I, La Marra M, Allocca S, et al. The role of nutraceutical supplements, monacolin K and astaxanthin, and diet in blood cholesterol homeostasis in patients with myopathy. Biomolecules 2022; 12(8): 1118.
77
Villano I, La Marra M, Messina A, Di Maio G, Moscatelli F, Chieffi S. Effects of vegetarian and vegan nutrition on body composition in competitive futsal athletes. Prog Nutr 2021; 23: e2021126.
78
Bou Khalil R, El Hachem C. Potential role of repetitive transcranial magnetic stimulation in obesity. Eat Weight Disord 2014; 19(3): 403-7.
79
Francavilla VC, Genovesi F, Asmundo A, et al. Fascia and movement: The primary link in the prevention of accidents in soccer. Revision and models of intervention. Med Sport 2020; 73(2)
80
Praharaj SK, Mishra BR, Sarkar S, Mehta V, Diwedi S, Nizamie SH. Repetitive transcranial magnetic stimulation in psychiatry. Ann Indian Acad Neurol 2011; 14(4): 245-51.
81
Uher R, Yoganathan D, Mogg A, et al. Effect of left prefrontal repetitive transcranial magnetic stimulation on food craving. Biol Psychiatry 2005; 58(10): 840-2.
82
Barth KS, Rydin-Gray S, Kose S, et al. Food cravings and the effects of left prefrontal repetitive transcranial magnetic stimulation using an improved sham condition. Front Psychiatry 2011; 2: 9.
83
Jansen JM, Daams JG, Koeter MWJ, Veltman DJ, van den Brink W, Goudriaan AE. Effects of non-invasive neurostimulation on craving: A meta-analysis. Neurosci Biobehav Rev 2013; 37(10): 2472-80.
84
Levin BE. Why some of us get fat and what we can do about it. J Physiol 2007; 583(2): 425-30.
85
May J, Andrade J, Kavanagh DJ, Hetherington M. Elaborated intrusion theory: A cognitive-emotional theory of food craving. Curr Obes Rep 2012; 1(2): 114-21.
86
Sobik L, Hutchison K, Craighead L. Cue-elicited craving for food: A fresh approach to the study of binge eating. Appetite 2005; 44(3): 253-61.
87
Pelchat ML, Schaefer S. Dietary monotony and food cravings in young and elderly adults. Physiol Behav 2000; 68(3): 353-9.
88
Hill AJ. The psychology of food craving. Proc Nutr Soc 2007; 66(2): 277-85.
89
Wurtman RJ, Wurtman JJ. Carbohydrate craving, obesity and brain serotonin. Appetite 1986; 7(Suppl.): 99-103.
90
Lafay L, Thomas F, Mennen L, et al. Gender differences in the relation between food cravings and mood in an adult community: Results from the Fleurbaix Laventie Ville Sant� Study. Int J Eat Disord 2001; 29(2): 195-204.
91
Vander Wal JS, Johnston KA, Dhurandhar NV. Psychometric properties of the state and trait food cravings questionnaires among overweight and obese persons. Eat Behav 2007; 8(2): 211-23.
92
Volkow ND, Wang GJ, Tomasi D, Baler RD. The addictive dimensionality of obesity. Biol Psychiatry 2013; 73(9): 811-8.
93
Wang GJ, Volkow ND, Thanos PK, Fowler JS. Similarity between obesity and drug addiction as assessed by neurofunctional imaging: A concept review. J Addict Dis 2004; 23(3): 39-53.
94
Pannacciulli N, Le DSNT, Chen K, Reiman EM, Krakoff J. Relationships between plasma leptin concentrations and human brain structure: A voxel-based morphometric study. Neurosci Lett 2007; 412(3): 248-53.
95
Pannacciulli N, Del Parigi A, Chen K, Le DSNT, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: A voxel-based morphometric study. Neuroimage 2006; 31(4): 1419-25.
96
Karhunen LJ, Vanninen EJ, Kuikka JT, Lappalainen RI, Tiihonen J, Uusitupa MIJ. Regional cerebral blood flow during exposure to food in obese binge eating women. Psychiatry Res Neuroimaging 2000; 99(1): 29-42.
97
Amo Usanos C, Valenzuela PL, de la Villa P, et al. Neuromodulation of the prefrontal cortex facilitates diet-induced weight loss in midlife women: A randomized, proof-of-concept clinical trial. Int J Obes 2020; 44(3): 568-78.
98
Chen J, Qin J, He Q, Zou Z. A meta-analysis of transcranial direct current stimulation on substance and food craving: What effect do modulators have? Front Psychiatry 2020; 11: 598.
99
Chieffi S, Villano I, Messina A, Monda V, La Marra M, Messina G. Involvement of orexin in sleep disorders and neurodegenerative diseases. Curr Top Pept Protein Res 2015; 16: 49-54.
100
Val-Laillet D, Aarts E, Weber B, et al. Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity. Neuroimage Clin 2015; 8: 1-31.
101
Villano I, La Marra M, Di Maio G, et al. Physiological role of orexinergic system for health. Int J Environ Res Public Health 2022; 19(14): 8353.
102
Polito R, Francavilla VC, Ambrosi A, et al. - Winter conferences of sports science 2020; 2020
103
MESSINA A. Role of the orexin system on arousal, attention, feeding behaviour and sleep disorders. Acta Med Mediter 2017; 645–9
104
Fonteneau C, Redoute J, Haesebaert F, et al. Frontal transcranial direct current stimulation induces dopamine release in the ventral striatum in human. Cereb Cortex 2018; 28(7): 2636-46.
105
Di Maio G, Villano I, Ilardi CR, et al. Mechanisms of transmission and processing of pain: A narrative review. Int J Environ Res Public Health 2023; 20(4): 3064.
106
Ilardi CR, di Maio G, Villano I, et al. The assessment of executive functions to test the integrity of the nigrostriatal network: A pilot study. Front Psychol 2023; 14: 1121251.
107
La Marra M, Messina A, Ilardi CR, et al. The neglected factor in the relationship between executive functioning and obesity: The role of motor control. Healthcare 2022; 10(9): 1775.
108
Peanlikhit T, Van Waes V, Pedron S, et al. The antidepressant-like effect of tDCS in mice: A behavioral and neurobiological characterization. Brain Stimul 2017; 10(4): 748-56.
109
Taber MT, Fibiger HC. Electrical stimulation of the prefrontal cortex increases dopamine release in the nucleus accumbens of the rat: Modulation by metabotropic glutamate receptors. J Neurosci 1995; 15(5): 3896-904.
110
Ceccanti M, Inghilleri M, Attilia ML, et al. Deep TMS on alcoholics: Effects on cortisolemia and dopamine pathway modulation. A pilot study. Can J Physiol Pharmacol 2015; 93(4): 283-90.
111
Volkow ND, Wang GJ, Baler RD. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci 2011; 15(1): 37-46.
112
Wang GJ, Volkow ND, Logan J, et al. Brain dopamine and obesity. Lancet 2001; 357(9253): 354-7.
113
Messina G, Viggiano A, Chieffi S, La Marra M, Esposito T, De Luca V. Interaction between leptin and fat and its relationship to menopause. Curr Top Pept Protein Res 2011; 12: 77-81.
114
Sutin AR, Zonderman AB, Uda M, et al. Personality traits and leptin. Psychosom Med 2013; 75(5): 505-9.
115
Gabrieli JDE, Ghosh SS, Whitfield-Gabrieli S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 2015; 85(1): 11-26.
116
Schmidt U, Campbell IC. Treatment of eating disorders can not remain ‘brainless’: The case for brain-directed treatments. Eur Eat Disord Rev 2013; 21(6): 425-7.
117
Burger KS, Berner LA. A functional neuroimaging review of obesity, appetitive hormones and ingestive behavior. Physiol Behav 2014; 136: 121-7.
118
Goldman RL, Borckardt JJ, Frohman HA, et al. Prefrontal cortex transcranial direct current stimulation (tDCS) temporarily reduces food cravings and increases the self-reported ability to resist food in adults with frequent food craving. Appetite 2011; 56(3): 741-6.
119
Stevens J, McClain JE, Truesdale KP. Selection of measures in epidemiologic studies of the consequences of obesity. Int J Obes 2008; 32(S3)(Suppl. 3): S60-6.
120
Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143(3): 228-39.
121
Rothman KJ. BMI-related errors in the measurement of obesity. Int J Obes 2008; 32(S3)(Suppl. 3): S56-9.
122
Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S. Race-ethnicity–specific waist circumference cutoffs for identifying cardiovascular disease risk factors1–3. Am J Clin Nutr 2005; 81(2): 409-15.
123
Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004; 79(3): 379-84.
124
Price GM, Uauy R, Breeze E, Bulpitt CJ, Fletcher AE. Weight, shape, and mortality risk in older persons: Elevated waist-hip ratio, not high body mass index, is associated with a greater risk of death. Am J Clin Nutr 2006; 84(2): 449-60.
125
Restrepo BJ. Obesity prevalence among U.S. Adults during the COVID-19 pandemic. Am J Prev Med 2022; 63(1): 102-6.
126
Chieffi S, Messina G, La Marra M, Iavarone A, Viggiano A, De Luca V. Distractor interference in visual motor tasks. Horizons in Neuroscience Research 2014; 13: 151-60.
127
Bezdjian S, Baker LA, Lozano DI, Raine A. Assessing inattention and impulsivity in children during the Go/NoGo task. Br J Dev Psychol 2009; 27(2): 365-83.
128
Giambra LM, Quilter RE. Sex differences in sustained attention across the adult life span. J Appl Psychol 1989; 74(1): 91-5.
129
Riley N, Lubans DR, Holmes K, Morgan PJ. Findings from the EASY minds cluster randomized controlled trial: Evaluation of a physical activity integration program for mathematics in primary schools. J Phys Act Health 2016; 13(2): 198-206.
130
Barnett JH, Jones PB, Robbins TW, Müller U. Effects of the catechol-O-methyltransferase Val158Met polymorphism on executive function: A meta-analysis of the Wisconsin Card Sort Test in schizophrenia and healthy controls. Mol Psychiatry 2007; 12(5): 502-9.
131
Lange EH, Nesvåg R, Ringen PA, et al. One year follow-up of alcohol and illicit substance use in first-episode psychosis: Does gender matter? Compr Psychiatry 2014; 55(2): 274-82.
132
Chieffi S, Castaldi C, Di Maio G, et al. Attentional bias in the radial and vertical dimensions of space. C R Biol 2019; 342(3-4): 97-100.
133
Ilardi CR, La Marra M, Amato R, et al. The “Little Circles Test” (LCT): A dusted-off tool for assessing fine visuomotor function. Aging Clin Exp Res 2023; 35(11): 2807-20.
134
Wang GJ, Volkow ND, Telang F, et al. Evidence of gender differences in the ability to inhibit brain activation elicited by food stimulation. Proc Natl Acad Sci USA 2009; 106(4): 1249-54.
135
Cahill L. Why sex matters for neuroscience. Nat Rev Neurosci 2006; 7(6): 477-84.
136
Cummings JL. Frontal-subcortical circuits and human behavior. Arch Neurol 1993; 50(8): 873-80.
137
Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci 2001; 24(1): 167-202.
138
Bonté E, Flemming T, Fagot J. Executive control of perceptual features and abstract relations by baboons (Papio papio). Behav Brain Res 2011; 222(1): 176-82.
139
Mansouri FA, Tanaka K, Buckley MJ. Conflict-induced behavioural adjustment: A clue to the executive functions of the prefrontal cortex. Nat Rev Neurosci 2009; 10(2): 141-52.
140
Verbruggen F, Logan GD. Response inhibition in the stop-signal paradigm. Trends Cogn Sci 2008; 12(11): 418-24.
141
Del Parigi A, Gautier JF, Chen K, et al. Neuroimaging and obesity. Ann N Y Acad Sci 2002; 967(1): 389-97.
142
Ruberto M, Monda V, Precenzano F, Maio GD, Messina A, Lanzara V. Physical activity, ketogenic diet, and epilepsy: A mini-review. Sport Mont 2021; 19(1): 109-13.
143
Bikson M, Bestmann S, Edwards D. Transcranial devices are not playthings. Nature 2013; 501(7466): 167-7.
144
Truong DQ, Magerowski G, Blackburn GL, Bikson M, Alonso-Alonso M. Computational modeling of transcranial direct current stimulation (tDCS) in obesity: Impact of head fat and dose guidelines. Neuroimage Clin 2013; 2: 759-66.