Differential diagnosis of bipolar II disorder and major depressive disorder: Integrating multimodal approaches to overcome clinical challenges

Abstract

Clinically differentiating bipolar II disorder (BD-II) from major depressive disorder (MDD) remains a significant challenge in modern psychiatry. These two conditions share substantial clinical symptomatology, making accurate diagnosis difficult in routine clinical practice. Misdiagnosis may lead to inappropriate treatment strategies, increased psychological and physical burdens, reduced quality of life, and impaired social functioning. Genetic overlap may partially explain the clinical similarities between MDD and BD-II, and biomarkers along with neuroimaging techniques are receiving increasing attention as tools to aid in diagnosis. For example, electroencephalography has been shown to effectively distinguish between unipolar depression and bipolar depression; serum levels of glycogen synthase kinase-3 have also been investigated as a potential tool for differentiating between the two disorders. A comprehensive assessment integrating clinical characteristics, genetic basis research, and multimodal evaluations using neuroimaging and biomarkers through a multidisciplinary approach will help enhance clinicians' ability to distinguish between MDD and BD-II. By improving diagnostic accuracy, more personalized and effective treatment strategies can be developed, ultimately improving patients' health outcomes and quality of life.

Key Words: Bipolar II disorder; Major depressive disorder; Clinical symptoms; Biomarkers; Neuroimaging

Core Tip: A multidisciplinary approach that combines detailed clinical evaluation, insights into genetic underpinnings, and multimodal assessments using neuroimaging and biomarkers may substantially improve clinicians’ ability to distinguish between major depressive disorder (MDD) and bipolar II disorder (BD-II). Enhanced diagnostic accuracy can lead to more tailored and effective treatment strategies, ultimately improving patients' health outcomes and quality of life. This mini-review aims to summarize current evidence on the distinguishing features of MDD and BD-II-focusing on clinical symptoms, genetic profiles, neuroimaging findings, and biomarker signatures-and discusses the potential of machine learning approaches to further refine and support the differential diagnostic process.

INTRODUCTION

The differential diagnosis between major depressive disorder (MDD) and bipolar II disorder (BD-II) remains a significant challenge in modern psychiatry. These two disorders exhibit considerable overlap in clinical symptoms, often making accurate diagnosis difficult in routine clinical practice. Misidentifying MDD as BD-II or vice versa-can result in inappropriate treatment strategies, increased psychological and physical burdens, and reduced quality of life and social functioning. Therefore, timely and accurate differential diagnosis is critical to ensuring optimal patient outcomes.

One of the primary reasons for misdiagnosis is the similarity in clinical presentation, particularly during depressive episodes. Patients with BD-II often exhibit symptoms that closely resemble those of unipolar depression, leading to frequent diagnostic errors, especially in individuals presenting for the first time[1]. In addition, emerging evidence suggests a degree of genetic overlap between MDD and BD-II, which may partly explain their clinical similarities[2]. For example, a genome-wide association study involving 185285 individuals with depression and 439741 controls identified several genetic loci significantly associated with both disorders. This finding points to a potential shared genetic foundation. Elucidating these genetic mechanisms not only enhances our understanding of the pathophysiology of mood disorders but also offers promising avenues for the development of novel biomarkers to aid differential diagnosis.

In recent years, biomarkers and neuroimaging techniques have gained increasing attention as valuable adjuncts in the diagnostic process. Objective measures such as electroencephalography (EEG) have been shown to effectively differentiate between unipolar and bipolar depression. EEG-based studies have identified distinct patterns of brain electrical activity in patients with BD-II compared to those with MDD, providing a novel perspective for clinical assessment[3]. Additionally, various biological markers-such as serum levels of glycogen synthase kinase-3 (GSK3)-have been explored as potential tools for distinguishing between the two disorders. Preliminary findings indicate that GSK3β may hold particular promise in aiding the diagnosis of BD-II[1].

A multidisciplinary approach that combines detailed clinical evaluation, insights into genetic underpinnings, and multimodal assessments using neuroimaging and biomarkers may substantially improve clinicians’ ability to distinguish between MDD and BD-II. Enhanced diagnostic accuracy can lead to more tailored and effective treatment strategies, ultimately improving patients' health outcomes and quality of life. This mini-review aims to summarize current evidence on the distinguishing features of MDD and BD-II-focusing on clinical symptoms, genetic profiles, neuroimaging findings, and biomarker signatures-and discusses the potential of machine learning (ML) approaches to further refine and support the differential diagnostic process.

METHODOLOGY

A search was conducted on PubMed using the search terms "Bipolar II Disorder” and “Major Depressive Disorder". The focus was placed not only on clinical presentations but also on studies reporting objective differential diagnostic markers and novel research methodologies. Therefore, the timeframe was limited to 2020-2025, and the document types were restricted to clinical studies, reviews, and guidelines. Studies involving differential diagnosis were included for discussion in this review. For specific populations where randomized controlled trials (RCTs) were available, studies with lower levels of evidence were excluded. In the absence of RCTs for a specific population, only the study with the largest sample size was analyzed. When inconsistent findings were identified within the same population group, all relevant studies were included for discussion. As this is a descriptive review rather than a systematic review, no formal quality assessment or bias analysis was performed on the included literature; instead, the limitations of the studies are discussed. Bipolar I disorder is not within the scope of this manuscript.

THE OVERLAP OF SYMPTOMS AND LIMITATIONS OF DIAGNOSTIC CRITERIA BETWEEN BD-II AND MDD

The overlap of symptoms and limitations of diagnostic criteria between BD-II and MDD complicates clinical differential diagnosis. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases, Eleventh Revision, there are many similar symptoms between BD-II and MDD, such as low mood, sleeplessness, changes in appetite, and difficulty concentrating[4]. This overlap leads to many BD-II patients being misdiagnosed as MDD and subsequently receiving inappropriate antidepressant treatment, which may worsen their condition[4]. Furthermore, the "mixed features" criterion introduced in DSM-5 attempts to address this issue, but there are still difficulties in recognizing hypomanic symptoms, resulting in many patients not receiving accurate diagnosis and treatment[5].

The difficulty in recognizing hypomanic symptoms is one of the main challenges in the differential diagnosis of BD-II and MDD. Studies show that over 60% of undiagnosed depressed patients exhibit hypomanic symptoms, which are often overlooked or misinterpreted as manifestations of depression[6]. In clinical practice, characteristics of hypomania such as emotional instability, irritability, and increased activity are often viewed as part of typical depressive symptoms, leading to missed diagnoses of BD-II[7]. This confusion results in treatments that do not align with the actual illness of the patient, thereby affecting treatment outcomes and prognosis[4].

Existing clinical assessment tools also have limitations in distinguishing between BD-II and MDD. For example, the Hamilton Depression Scale (HAMD) and the Young Mania Rating Scale (YMRS) may not effectively differentiate between these two disorders when assessing depressive and manic symptoms[8]. One study indicated that different definitions of YMRS show significant differences in results when assessing mixed features, and there is a low concordance with the mixed features criteria of DSM-5[8]. Additionally, existing tools often overlook individual differences and symptom diversity in patients, leading to an inability to comprehensively reflect the patient's condition.

In summary, the overlap of symptoms between BD-II and MDD, along with the limitations of current diagnostic criteria, poses challenges for the differential diagnosis of these two mood disorders in clinical practice. Addressing this issue by improving existing diagnostic tools and criteria, adequately considering the recognition of hypomanic symptoms, and increasing clinicians' vigilance will help enhance the diagnostic accuracy for BD-II patients, thereby optimizing treatment strategies.

DIFFERENCES IN COURSE CHARACTERISTICS AND DIAGNOSTIC DELAYS BETWEEN BD-II AND MDD

BD-II and MDD exhibit significant differences in terms of episode frequency, duration, and symptom evolution. BD-II typically presents as alternating episodes of depressive episodes and hypomanic episodes, while MDD primarily manifests as a persistent state of severe depression. Research shows that depressive episodes in BD-II patients often last shorter than those in MDD patients, but the frequency of episodes may be higher[9]. The symptom evolution in BD-II is usually nonlinear, with fluctuations in mood and energy, complicating its diagnosis. On the other hand, MDD patients typically present more stable depressive symptoms, making the identification of MDD relatively more straightforward. Therefore, based on these differences in course characteristics, clinicians face significant challenges in differentiating between these two disorders, especially in cases of atypical or overlapping symptoms.

The impact of diagnostic delays on patient prognosis is also an important issue. Due to the similarity of symptoms between BD-II and MDD, BD-II is often misdiagnosed or diagnosed late, which may lead to inappropriate treatment and worsen the condition. Studies show that early identification of BD-II can significantly improve treatment outcomes and quality of life for patients[10]. Conversely, delayed diagnosis may result in patients experiencing multiple depressive episodes, increasing the risk of suicide and causing long-term effects on the overall mental health of the patient. Therefore, timely and accurate diagnosis is crucial for improving the prognosis of BD-II patients.

Existing longitudinal assessment methods have certain limitations in identifying the differences between BD-II and MDD. Many traditional assessment tools, such as the HAMD and the Composite International Diagnostic Interview, while having some effectiveness in assessing depression, fall short in recognizing the characteristics of bipolar disorder (BD)[11]. Especially in cases of dynamic changes in the course of the illness, traditional methods often fail to capture changes in the patient's status in a timely manner.

GENETICS AND NEUROBIOLOGY OF BD-II AND DEPRESSION

From a genetic perspective, the risk of developing MDD and BD-II is strongly associated with family history. Research by McCarron et al[12] and Cristancho et al[13] indicates that families of individuals with BD-II often have a history of other psychiatric disorders, more so than in families of individuals with MDD. A long-term follow-up study on high-risk familial populations has shown that genetic factors play a significant role in the development of BD, particularly among individuals whose families have a history of BD, where the risk of conversion is higher[14]. Specifically, genetic risk assessments can help identify patients with unipolar depression who may be at risk of developing BD in the future, thereby providing clinicians with an earlier window for intervention.

From a neurobiological standpoint, patients with MDD often exhibit imbalances in neurotransmitter systems, particularly those involving dopamine, norepinephrine, and serotonin. In contrast, BD-II may be associated with hyperactivity in these same neurotransmitter systems, leading to abnormally elevated moods[15,16]. Furthermore, brain imaging studies comparing MDD and BD-II have revealed significant differences in the activity patterns of the prefrontal cortex and limbic system, suggesting distinct underlying pathophysiological mechanisms[17,18].

THE ROLE OF BIOMARKERS IN DIFFERENTIAL DIAGNOSIS

Biomarkers play a crucial role in the differential diagnosis between MDD and BD (Table 1). A retrospective study involving 182 patients-65 with mania, 34 with bipolar depression, and 83 with unipolar depression-revealed that levels of interleukin-6 and tumor necrosis factor-alpha are generally higher in patients with BD compared to those with unipolar depression[19]. Metabolites of neurotransmitters, such as changes in serotonin and norepinephrine levels, may be associated with the episodic mood swings characteristic of BD[20].

Table 1 Key biomarkers in differentiating bipolar II disorder from major depressive disorder.

Biomarker/category	MDD	BD-II	Ref.
Inflammatory cytokines: IL-6, TNF-α	Lower or normal compared to BD	Generally elevated compared to MDD	[19]
Neurotransmitter metabolites: Serotonin, norepinephrine	Levels relatively stable or altered without clear episodic pattern	Fluctuations associated with mood episodes	[20]
Neutrophil-to-lymphocyte ratio	Lower than BD and close to healthy controls	Significantly elevated; correlated with frequency and severity of depressive episodes	[21,22]
Anti-inflammatory cytokine: IL-10	Consistently reduced; linked to impaired suppression of inflammation	Marked variability: Lower during depressive episodes, higher or normalized during hypomanic episodes	[23,24]
Inflammatory cell counts	Leukocyte and NLR within normal or slightly elevated range	Leukocyte and NLR significantly elevated	[25]
Oxidative stress markers: ROS, antioxidant enzymes (SOD, CAT)	ROS low/normal; antioxidant enzyme activity preserved or slightly elevated	ROS elevated; antioxidant enzyme activity reduced	[26]
Cortisol levels	Less pronounced fluctuation; moderate increase under stress	Greater fluctuation; significantly elevated during depressive episodes; distinct pattern from other depression types	[27-29]

MDD: Major depressive disorder; BD-II: Bipolar II disorder; ROS: Reactive oxygen species; NLR: Neutrophil-to-lymphocyte ratio; TNF-α: Tumor necrosis factor-α; IL-6: Interleukin-6; IL-10: Interleukin-10; SOD: Superoxide dismutase; CAT: Catalase; BD: Bipolar disorder.

In addition, the neutrophil-to-lymphocyte ratio (NLR), as an inflammatory marker, has recently been widely used to investigate biological differences in mood disorders. Research findings show that NLR is significantly elevated in individuals with BD compared to both unipolar depression patients and healthy controls (HC)[21], and increased NLR levels correlate with the frequency and severity of depressive episodes[22]. Due to its simple measurement and low cost, NLR can serve as one of the monitoring indicators for regular follow-ups.

Interleukin-10 (IL-10) is a crucial anti-inflammatory cytokine that regulates immune responses and suppresses inflammation. Multiple studies have demonstrated that MDD patients tend to have reduced IL-10 levels, which may be closely related to the pathogenesis of depression. Specifically, decreased IL-10 may weaken the body's ability to inhibit inflammatory responses, thereby exacerbating depressive symptoms[23]. In contrast, BD-II patients display greater variability in IL-10 levels, potentially linked to different illness phases (e.g., depressive vs hypomanic episodes). During depressive episodes, IL-10 levels may fall below the normal range, whereas during hypomanic episodes, IL-10 levels may rise or return to baseline[24].

Another study on inflammatory markers found that the leukocyte count and the NLR were significantly higher in BD patients than in those with unipolar depression, suggesting a key role of inflammation in BD[25].

One study reported that serum reactive oxygen species levels were markedly elevated in BD-II patients, accompanied by reduced activity of antioxidant enzymes, potentially leading to neuronal damage and dysfunction[26]. In contrast, depressive patients showed relatively preserved antioxidant capacity; some studies even reported superoxide dismutase and catalase activities comparable to, or slightly higher than, HC[26].

During both acute episodes and remission, BD-II patients have been observed to exhibit pronounced fluctuations in cortisol levels. Compared with HC, BD-II patients experience persistently elevated cortisol during depressive phases, possibly due to ongoing psychological stress and anxiety[27]. Furthermore, BD-II patients demonstrate distinct patterns of cortisol and other biomarker changes compared to other types of depression, particularly showing greater cortisol elevation during acute mood episodes, which may be directly related to emotional instability[28,29].

THE DISCRIMINATORY ROLE OF IMAGING AND MULTIMODAL DIAGNOSTIC TECHNIQUES

With the application of functional magnetic resonance imaging (fMRI), computed tomography, and positron emission tomography (PET), there has been significant progress in comprehensively understanding changes in the structure and function of patients' brains, thereby enhancing diagnostic accuracy. A systematic review of 60 neuroimaging studies indicated that the main abnormalities in patients with BD were found in the frontal gyrus, anterior cingulate cortex, and posterior cingulate cortex, with differences also observed in emotion- and reward-related networks. Cerebellar (vermis) to cerebral functional connectivity was identified as the most common finding in BD. Furthermore, the connectivity between the prefrontal cortex and amygdala, as part of the rich-club hubs, was frequently reported to be disrupted. The most common findings based on effective connectivity were alterations in the salience network, default mode network, and executive control network[30].

Multimodal imaging can simultaneously provide anatomical and functional information, improving early detection and classification capabilities for BD[31]. During the treatment process for BD patients, imaging is used to monitor and assess the impact of medications on brain activity in real-time, allowing for adjustments in treatment plans. Additionally, imaging monitoring can be utilized to evaluate whether patients undergoing antidepressant therapy experience mood shifts, thus reducing treatment-related risks[32].

Structural magnetic resonance imaging (sMRI) is primarily used to assess changes in brain structures. Studies have shown that hippocampal atrophy and shrinkage of the prefrontal cortex are common in depressed patients, whereas BD patients may exhibit different patterns of regional volume changes, which are associated with disease progression and episode frequency[33]. By integrating results from sMRI and fMRI, clinicians can gain a more comprehensive understanding of the patient's pathophysiological state, thereby optimizing treatment strategies.

fMRI and sMRI indicate significant alterations in regions responsible for emotion regulation and cognitive control in BD patients. During depressive episodes, compared to HC, BD patients show markedly different activation levels in areas such as the prefrontal cortex and anterior cingulate cortex[34]. When experiencing depressive episodes, BD patients exhibit significantly increased functional connectivity within the frontal and limbic systems, whereas depressed patients often show a decrease, indicating fundamental differences in the neural mechanisms underlying emotional processing and cognitive functions between the two conditions[31,35,36]. Moreover, BD patients display varying patterns of functional connectivity across different states-depression, mania, and euthymia-which reflects their unique neurobiological characteristics[37,38]. Furthermore, studies suggest that fMRI data can distinguish between BD and unipolar depression patients with over 98% accuracy[38], aiding in preliminary diagnosis before patients experience pronounced manic or depressive episodes.

BD and MDD exhibit significant differences in their metabolic profiles, and it is metabolic imaging techniques-particularly PET-that provide us with deeper insights. Research has shown that BD patients display abnormal glucose metabolism in the prefrontal cortex, typically characterized by hypometabolism, which may be associated with mood fluctuations and cognitive impairments. Specifically, reduced glucose metabolism in the prefrontal cortex may reflect insufficient energy demand during emotional regulation processes, thereby affecting emotional stability and cognitive performance[39]. In contrast, MDD patients show altered 5-HT (serotonin) receptor binding potential in the limbic system, changes that correlate with the severity of depressive symptoms. Decreased 5-HT receptor binding potential in the limbic system may exacerbate symptoms such as low mood and anxiety[40]. Furthermore, differences in dopaminergic system function between BD and MDD also hold diagnostic significance: BD patients show increased availability of dopamine transporters, whereas MDD patients exhibit reduced dopamine transporter availability, which may be linked to distinct underlying mechanisms in emotional regulation and the reward system in the two disorders[41].

Additionally, multimodal imaging combined with ML enables the integration of complementary information from various imaging datasets, optimizing the extraction of features related to brain function and structure. The performance of patients on tasks involving emotion regulation and social cognition is closely linked to specific functional connections in brain regions, not only enhancing the sensitivity and specificity of diagnosis but also potentially paving new directions for personalized treatment in the future[42].

THE ROLE OF EEG IN DIFFERENTIATING BD-II AND MDD

EEG signals can be divided into multiple frequency bands, primarily including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) waves. Each frequency band is associated with specific neurofunctional states and cognitive processes, reflecting different patterns of brain activity. EEG can capture real-time brain electrical activity, and its high temporal resolution and cost-effectiveness make it a valuable tool for studying the neural mechanisms underlying mood disorders (Table 2).

Table 2 Comparative electroencephalography features in bipolar II disorder vs major depressive disorder.

Feature category	BD-II characteristics	MDD characteristics	Clinical implication	Ref.
Power Spectrum (Alpha Band)	Resting-state alpha power close to healthy controls; no significant reduction	Significant alpha power reduction in centro-parietal regions; negatively correlated with symptom severity	Alpha power reduction may indicate MDD	[43,45]
Power Spectrum (Theta Band)	Reduced spontaneous phase variability in frontal theta	Reduced spontaneous phase variability in frontal theta	Theta reduction is a shared feature	[43]
Phase Dynamics (Alpha Band)	No significant alpha phase delay; stable phase variability	Pronounced alpha phase-cycle delay; reduced phase variability	Alpha phase delay may be MDD-specific	[43,49]
Mood-State Dependency	EEG patterns close to healthy controls during euthymic state; altered patterns during mood episodes	Abnormal EEG patterns even at rest	Reflects state-related differences between disorders	[43,45]
Machine Learning Classification	EEG features can be classified by SVM/KNN with approximately 93% accuracy	Same as BD-II	Enhances diagnostic accuracy	[50]

KNN: K-Nearest Neighbors; MDD: Major depressive disorder; EEG: Electroencephalography; SVM: Support vector machines; BD-II: Bipolar II disorder.

Researchers have attempted to distinguish BD-II from MDD using EEG power spectral analysis. However, power analysis alone is insufficient for fully differentiating these two mood disorders[43]. Consequently, recent studies have shifted attention to EEG phase dynamics, particularly phase variability and phase lag in the theta and alpha frequency bands-metrics that show potential for distinguishing BD-II from MDD[43,44].

In terms of power spectrum findings, MDD patients often show significantly reduced alpha power in the centro-parietal regions, which is negatively correlated with the severity of depressive symptoms, reflecting abnormal cortical activity and reduced efficiency of information processing[43]. In contrast, BD-II patients do not exhibit significant alpha power reduction, and their resting-state EEG characteristics are similar to those of HC. This may be related to adaptive mechanisms in emotional regulation[45]. However, there is overlap in EEG patterns between BD-II and MDD during depressive episodes, and both are highly susceptible to noise and artifacts, which reduces diagnostic discrimination[46-48].

Regarding phase dynamics, potentially discriminative indicators include the phase locking value and the phase lag index. Both BD-II and MDD patients show reduced spontaneous phase variability in the frontal theta band compared to HC, suggesting functional impairment in emotion regulation networks[43]. MDD patients also show decreased phase variability in the centro-parietal alpha band along with delayed alpha phase cycles, which are negatively correlated with depressive symptom severity[43,49]. In contrast, BD-II patients generally do not exhibit significant alpha phase delays, and their phase dynamics remain relatively stable[43,49]. Clinically, alpha phase delay may serve as an MDD-specific biomarker, whereas reduced theta phase variability appears to be a shared feature of both disorders.

In terms of region-specific EEG changes, MDD: Reduced centro-parietal alpha power, alpha phase delay; BD-II: Reduced frontal theta phase variability, but stable alpha phase; EEG patterns during euthymic periods resemble HC[43,45].

Furthermore, ML approaches-such as Support Vector Machines (SVM) and k-Nearest Neighbors-can classify EEG features to distinguish BD-II from MDD, achieving classification accuracy rates of up to 93%[50].

APPLICATION OF ML IN THE DIFFERENTIAL DIAGNOSIS OF BD-II AND MDD

In recent years, ML has demonstrated remarkable potential in the field of psychiatric diagnosis. By analyzing multidimensional data, ML techniques can improve diagnostic accuracy, reduce misdiagnosis rates, and uncover potential pathological patterns, thereby providing decision-support tools for clinicians[6]. Its applications encompass not only the discovery of biomarkers but also the analysis of behavioral traits, language patterns, neuroimaging indicators, and the integration of multimodal information.

In genomics and genetic risk assessment, network analysis has revealed differences in neuroplasticity between psychiatric disorders, finding that BD patients exhibit higher levels of plasticity than MDD patients, offering a novel approach for early differentiation[6]. Random forest (RF) and SVM have shown excellent performance in analyzing gene expression data, enabling the identification of gene combinations strongly associated with BD-II [area under the curve (AUC) up to 0.951][51-53]. Polygenic Risk Scores, which integrate disease-associated genetic variants to compute individual genetic risk, have achieved a sensitivity of 75% and specificity of 76% in predicting the future development of BD in pediatric populations[10].

In natural language, facial expression, and emotion recognition, natural language processing techniques combined with SVM and Linear Discriminant Analysis can detect features such as diversity in emotional vocabulary and changes in syntactic structure, effectively distinguishing BD-II from MDD[51,54,55]. Deep learning (CNN) can extract facial image features and match them to emotional categories (e.g., happiness, sadness, anger) to enable real-time monitoring[9,56]. The Facial Action Coding System combined with CNN allows for automated analysis of facial expression changes, effectively differentiating BD from MDD patients[52,55].

Feature-level fusion is an important approach to enhancing diagnostic performance. Early fusion integrates multisource data at the feature extraction stage-or example, combining MDQ and BSDS questionnaires for multivariate analysis achieved an AUC exceeding 0.8[9]. Late fusion combines results at the model output stage and is suitable for heterogeneous data. Hybrid models that integrate multiple algorithms can achieve 84% sensitivity and 82% specificity in distinguishing BD from MDD[55,57,58].

Overall, in psychiatric data analysis, different algorithms have distinct advantages and limitations. For example, SVM performs well on small-sample datasets but is sensitive to parameter tuning and computationally expensive with large datasets[51]. Ensemble methods such as RF often achieve higher accuracy by combining predictions from multiple models, thus reducing the bias and variance of individual models[53]. Furthermore, deep learning models excel in big data environments, especially for processing images and text, as they can extract more complex features and thereby improve classification accuracy[56]. However, algorithm performance in practice depends on factors such as sample size, feature selection, and data preprocessing. Therefore, selecting an appropriate algorithm requires not only considering the application scenario but also the characteristics of the dataset and research objectives. To improve the diagnostic accuracy between BD-II and MDD, leveraging the strengths of multiple algorithms through hybrid modeling may be an effective strategy[57,58] (Table 3).

Table 3 Characteristics and application scenarios of different machine learning algorithms in bipolar II disorder vs major depressive disorder differential diagnosis.

Algorithm type	Advantages	Limitations	Application scenarios	Example applications	Ref.
Support Vector Machine	Performs well on small, high-dimensional datasets; clear decision boundaries	Sensitive to parameter tuning; high computational cost for large datasets	Gene expression analysis; language pattern recognition	Distinguishing emotional text features between BD-II and MDD	[51,54,55]
Random Forest	Strong robustness; handles nonlinear relationships; provides feature importance ranking	May overfit small datasets	Multi-modal feature integration; questionnaire + imaging data	Gene combination screening; questionnaire-based classification	[52,53]
Deep Learning (CNN, RNN)	Automatically extracts complex features; suitable for image and text data	Requires large datasets and high computational power; low interpretability	Facial expression analysis; social media data	Micro-expression recognition; emotion classification	[9,52,55,56]
Linear Discriminant Analysis	Simple and efficient; high interpretability	Limited by linear assumptions; unsuitable for highly nonlinear data	Emotional vocabulary frequency analysis	Vocabulary-based emotion classification	[54,55]
Polygenic Risk Score	Integrates genetic information; enables personalized risk prediction	Depends on large-scale genomic data	Early risk prediction; pediatric cohort studies	Predicting future risk of BD development	[10]
Hybrid Models	Combines strengths of multiple algorithms; improved performance	Complex implementation; requires coordination between models	High-dimensional, multi-modal data analysis	SVM + RF combined diagnosis	[55,57,58]

MDD: Major depressive disorder; SVM: Support vector machines; BD-II: Bipolar II disorder; BD: Bipolar disorder; RF: Random forest.

Notably, ML not only achieves higher precision in discrimination within the traditional knowledge framework, but may also advance our understanding of MDD and BD-II. Maes and Stoyanov[59] have challenged conventional views on MDD and BD through a novel data-driven ML model termed "nomothetic network psychiatry" (NNP), sparking a new therapeutic revolution. Their research proposes that MDD and BD are not mind-brain disorders or psychosocial conditions, but rather unified systemic medical illnesses; it rejects symptom-based binary diagnoses and establishes entirely new therapeutic targets based on the immune-metabolic axis, oxidative damage cascades, and synergistic pathologies[59]. The study introduces "recurrence of illness" (ROI) as a unifying core, replacing traditional diagnostic categories-ROI mediates the pathological pathway from early-life trauma to nitro-oxidative stress and ultimately to the phenome of mood disorders, enabling a paradigm shift. For the first time, the NNP model provides a nomothetic pathological explanation and a mechanistic treatment framework for mood disorders, ending the era of symptom-aggregation-based diagnosis and laying the foundation for precision psychiatry.

DIFFERENCES IN COGNITIVE FUNCTION BETWEEN BD AND DEPRESSION

There are significant differences in cognitive function performance between BD and depression. Patients with BD exhibit more complex and severe cognitive deficits, particularly in areas such as executive function, attention, and information processing speed. This is related to the neurobiological basis of reduced functional connectivity in the prefrontal cortex[60,61]. Especially during depressive episodes, BD patients experience distraction and slowed information processing speeds, leading to significantly poorer performance on cognitive function tests compared to normal controls[18]. In contrast, cognitive impairments in depressed patients are relatively mild, mainly involving difficulties with concentration and memory issues, which may improve following mood stabilization[62]. Therefore, cognitive function assessment is crucial for distinguishing between BD and depression, aiding in the development of targeted treatment plans, such as cognitive behavioral therapy, to improve patient quality of life[63].

Cognitive function assessment has shown significant value in differentiating between BD and unipolar depression in clinical applications. Research indicates that cognitive impairment is more prevalent among BD patients, manifesting as deficits in information processing speed, memory, attention, and executive functions[37]. Standardized cognitive tests, such as the Wechsler Memory Scale and Trail Making Test, can effectively assess patient cognitive functions, providing support for differential diagnosis. In one study, researchers utilized fMRI and cognitive tests to explore differences in cognitive function between BD and unipolar depression patients. The results showed that BD patients performed notably lower than unipolar depression patients in areas such as emotion regulation and social cognition, offering new perspectives for early identification of BD[64]. Additionally, cognitive function assessments can help identify potential mixed features often overlooked during depressive episodes.

The role of cognitive rehabilitation as an adjunctive treatment is increasingly recognized. For BD patients, cognitive rehabilitation not only improves cognitive function but also alleviates depressive symptoms and enhances overall quality of life. Some studies suggest that combining cognitive behavioral therapy with cognitive training can effectively enhance the cognitive functions of BD patients, helping them better cope with daily challenges[37]. Implementing this comprehensive treatment model provides more thorough support for managing BD.

PSYCHOMOTOR DISTURBANCES IN DIFFERENTIATING MDD AND BD

Although bipolar depression and unipolar depression share similar clinical presentations, they fundamentally differ in their underlying neurobiological and psychological mechanisms. This superficial similarity may lead to overdiagnosis and consequently increase the risk of suicide. Recent research indicates that gait characteristics serve as a crucial objective marker for distinguishing between types of depression[65]. A study by Diana Bogdanova et al[66] compared psychomotor reactivity disorders and gait activity in patients with unipolar and bipolar depression, concluding that both psychomotor activity and reactivity in gait can serve as sensitive indicators for differentiating between similar psychiatric conditions. The study found that psychomotor inhibition is significantly more pronounced in patients with bipolar depression than in those with unipolar depression, and that both patient groups exhibit significantly lower levels compared to healthy norms. In assessing psychomotor disturbances, the complex task (COCE) was most sensitive to reaction capacity, while the simplified version (OE) was most sensitive to reactivity (reaction speed/frequency), with the standard task (CE) showing intermediate sensitivity. The simplified equilibriometric task emerged as the most sensitive assessment measure, with psychomotor reactivity proving more precise than motor performance alone in reflecting disease severity. The application of cranio-corpo-graph technology and the development of similar devices hold promise for opening new avenues in the early diagnosis and subtype prediction of depression. However, the study has certain limitations: Equipment constraints (fixed setup, narrow range, limited parameters, and reliance on laboratory settings); lack of a classification formula; absence of investigation into emotional/anxiety/attachment correlations; no tracking of medication effects; reliance on a single assessment method; dependence on expert diagnosis; and exclusion of cranial pathologies. These limitations highlight important directions for future research.

ANTIDEPRESSANT RESPONSE AND THE RISK OF TREATMENT-EMERGENT MANIA

In terms of treatment strategies, the standard approach for MDD typically includes antidepressants, psychotherapy, and lifestyle modifications, whereas BD-II is more commonly managed with mood stabilizers and antipsychotic medications[12,13]. Patients with MDD may demonstrate a more consistent response to antidepressants, while BD-II patients may experience worsening manic symptoms when treated with antidepressants, necessitating a more cautious approach during treatment[67,68].

However, existing studies indicate significant individual variability in response to antidepressant treatment, with some patients developing manic symptoms after starting antidepressant therapy. Based on an analysis of Danish health registry data involving 979 patients with bipolar depression, 358 of whom received antidepressant treatment, results showed no significant association between antidepressant use and the risk of manic episodes. Nevertheless, this does not imply that antidepressant use is entirely without risk[69]. In contrast, another study found a statistically significant link between antidepressant use and the occurrence of mania in certain patient populations[70].

CHALLENGES IN DIFFERENTIAL DIAGNOSIS AMONG SPECIAL POPULATIONS

In the differential diagnosis between depression and BD with hypomanic features, characteristics of special populations may complicate the diagnostic process. These groups typically include the elderly, children, and pregnant women, whose symptom presentation and drug response can significantly differ from the general population. For instance, elderly patients with depression might exhibit cognitive decline or more physical symptoms rather than typical low mood, potentially leading to underdiagnosis of BD[71]. Moreover, some studies suggest that the transition from depression to BD is relatively common among the elderly, necessitating heightened vigilance from clinicians regarding this shift[72].

The manifestation of BD in children also differs from adults; children are more likely to experience depressive episodes rather than clear manic episodes, often presenting with behavioral issues or attention deficits. These symptoms frequently overlap with other mental health problems, increasing the risk of misdiagnosis[54]. Additionally, children and adolescents undergoing antidepressant treatment are prone to emotional instability, which can trigger manic or hypomanic states, posing greater challenges for accurate clinical diagnosis[73].

Pregnancy complicates mental health assessment as well. Hormonal changes during pregnancy can lead to mood fluctuations, which might be mistaken for signs of depression or BD. Research indicates that depressive symptoms during pregnancy can vary significantly from those outside of pregnancy, underscoring the importance of appropriate evaluation and monitoring to avoid inappropriate treatment plans[74]. Furthermore, medication use during pregnancy requires particular caution to prevent adverse effects on the fetus.

The presence of comorbidities further complicates differential diagnosis. Conditions such as cardiovascular diseases and anxiety disorders are more prevalent among BD patients, impacting not only overall health but also emotional states. Clinicians must consider these comorbidities when assessing patients to develop personalized treatment plans.

In conclusion, diagnosing depression vs BD presents multifaceted challenges in special populations. Clinicians should enhance their awareness of these complexities, employ more detailed assessment tools, and integrate patient-specific factors for comprehensive diagnoses, thereby improving diagnostic accuracy and treatment efficacy.

CONCLUSION

BD-II and depression share highly overlapping clinical presentations, posing significant diagnostic challenges for clinicians. The innovation of this study lies in integrating various current differential diagnostic methods, including clinical characteristics, genetic studies, neuroimaging, and biomarker assessments, to overcome the limitations of traditional diagnostic tools in distinguishing BD-II from MDD. The application of ML algorithms has contributed to achieving an accuracy rate of up to 93%, providing a new approach for early and precise differentiation. Its clinical significance is in improving diagnostic accuracy, thereby promoting the development of personalized and more effective treatment strategies, which ultimately improves patients' quality of life and prognosis.

Future research urgently needs to integrate genetic data, neuroimaging findings, biomarkers, and clinical features to develop precise diagnostic models. Clinicians must enhance their ability to recognize BD-II and adopt evidence-based, individualized treatment approaches-especially for complex populations such as the elderly, pregnant women, and adolescents-to optimize therapeutic outcomes, improve prognosis, and enhance the quality of life for patients.